Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review
Abstract The medical condition known as acute lymphoblastic leukemia (ALL) is characterized by an excess of immature lymphocyte production, and it can affect people across all age ranges. Detecting it at an early stage is extremely important to increase the chances of successful treatment. Conventional diagnostic techniques for ALL, such as bone marrow and blood tests, can be expensive and time-consuming. They may be less useful in places with scarce resources. The primary objective of this research is to investigate automated techniques that can be employed to detect ALL at an early stage. This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. The effectiveness of these models in detecting ALL is evident through their ability to enhance accuracy and minimize human errors, which is essential for early diagnosis and successful treatment. In addition, the study also highlights several challenges and limitations in this field, including the scarcity of data available for ALL types, and the significant computational resources required to train and operate deep learning models.
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Acevedo, A., Alférez, S., Merino, A., Puigví, L., & Rodellar, J. (2019). Recognition of peripheral blood cell images using convolutional neural networks. Computer Methods and Programs in Biomedicine, 180, 105020. 10.1016/j.cmpb.2019.105020
Acharya, V., & Kumar, P. (2019). Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Medical & Biological Engineering & Computing, 57(8), 1783–1811. 10.1007/s11517-019-01984-1
Aftab, M. O., Javed Awan, M., Khalid, S., Javed, R., & Shabir, H. (2021). Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), 216–220. 10.1109/CAIDA51941.2021.9425264
Akter Hossain, M., Islam Sabik, M., Muntasir, I., Muzahidul Islam, A. K. M., Islam, S., & Ahmed, A. (2020). Leukemia Detection Mechanism through Microscopic Image and ML Techniques. 2020 IEEE REGION 10 CONFERENCE (TENCON), 61–66. 10.1109/TENCON50793.2020.9293925
Alam, A., & Anwar, S. (2021). Detecting Acute Lymphoblastic Leukemia Through Microscopic Blood Images Using CNN (pp. 207–214). 10.1007/978-981-33-6393-9_22
AL-Dulaimi, K., Banks, J., Nugyen, K., Al-Sabaawi, A., Tomeo-Reyes, I., & Chandran, V. (2021). Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review–Challenges, Current and Future Potential Techniques. IEEE Reviews in Biomedical Engineering, 14, 290–306. 10.1109/RBME.2020.3004639
Al-Qudah, R., & Suen, C. Y. (2020). Synthetic Blood Smears Generation Using Locality Sensitive Hashing and Deep Neural Networks. IEEE Access, 8, 102530–102539. 10.1109/ACCESS.2020.2999349
Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Hashim, M., Madhloom, H. T., Azeez, N. D., & Alsyisuf, S. (2018). A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations. Computer Methods and Programs in Biomedicine, 158, 93–112. 10.1016/j.cmpb.2018.02.005
Anilkumar, K. K., Manoj, V. J., & Sagi, T. M. (2022). Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning. IRBM, 43(5), 405–413. 10.1016/j.irbm.2021.05.005
Bhuiyan, Md. N. Q., Rahut, S. K., Tanvir, R. A., & Ripon, S. (2019). Automatic Acute Lymphoblastic Leukemia Detection and Comparative Analysis from Images. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 1144–1149. 10.1109/CoDIT.2019.8820299
Bibi, N., Sikandar, M., Ud Din, I., Almogren, A., & Ali, S. (2020). IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning. Journal of Healthcare Engineering, 2020, 1–12. 10.1155/2020/6648574
Bukhari, M., Yasmin, S., Sammad, S., & Abd El-Latif, A. A. (2022). A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning. Mathematical Problems in Engineering, 2022, 1–18. 10.1155/2022/2801227
Chen, E., Liao, R., Shalaginov, M. Y., & Zeng, T. H. (2022). Real-time Detection of Acute Lymphoblastic Leukemia Cells Using Deep Learning. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 3788–3790. 10.1109/BIBM55620.2022.9995131
Das, P. K., & Meher, S. (2021a). An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia. Expert Systems with Applications, 183, 115311. 10.1016/j.eswa.2021.115311
Das, P. K., & Meher, S. (2021b). Transfer Learning-Based Automatic Detection of Acute Lymphocytic Leukemia. 2021 National Conference on Communications (NCC), 1–6. 10.1109/NCC52529.2021.9530010
Das, P. K., Meher, S., Panda, R., & Abraham, A. (2022a). A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia. IEEE Access, 10, 81741–81763. 10.1109/ACCESS.2022.3196037
Das, P. K., Meher, S., Panda, R., & Abraham, A. (2022b). An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders. IEEE Transactions on Cybernetics, 52(10), 10615–10626. 10.1109/TCYB.2021.3062152
Das, P. K., Nayak, B., & Meher, S. (2022c). A lightweight deep learning system for automatic detection of blood cancer. Measurement, 191, 110762. 10.1016/j.measurement.2022.110762
Dese, K., Raj, H., Ayana, G., Yemane, T., Adissu, W., Krishnamoorthy, J., & Kwa, T. (2021). Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images. Clinical Lymphoma Myeloma and Leukemia, 21(11), e903–e914. 10.1016/j.clml.2021.06.025
Genovese, A., Hosseini, M. S., Piuri, V., Plataniotis, K. N., & Scotti, F. (2021a). Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1205–1209. 10.1109/ICASSP39728.2021.9414362
Genovese, G., Moltrasio, C., Berti, E., & Marzano, A. V. (2021b). Skin manifestations associated with COVID-19: current knowledge and future perspectives. Dermatology, 237(1), 1–12.
Genovese, A., Siegel, M., Diván, M., Piuri, V., Scotti, F., & János Viharos, Z. (2021c). Phone e-mail address TC assignments (joining year) TC mission-field of expertise (max. 1000 char. Including spaces).
Gupta, R., Gehlot, S., & Gupta, A. (2022). C-NMC: B-lineage acute lymphoblastic leukaemia: A blood cancer dataset. Medical Engineering & Physics, 103, 103793. 10.1016/j.medengphy.2022.103793
Hazarika, R. A., Abraham, A., Kandar, D., & Maji, A. K. (2021). An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images. IEEE Access, 9, 161194–161207. 10.1109/ACCESS.2021.3131741
Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2021). Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Systems with Applications, 167, 114161. 10.1016/j.eswa.2020.114161
Jha, K. K., & Dutta, H. S. (2019). Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images. Computer Methods and Programs in Biomedicine, 179, 104987. 10.1016/j.cmpb.2019.104987
Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019). A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification. 2019 International Conference on Information and Communication Technology Convergence (ICTC), 271–276. 10.1109/ICTC46691.2019.8939959
Khandekar, R., Shastry, P., Jaishankar, S., Faust, O., & Sampathila, N. (2021). Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis. Biomedical Signal Processing and Control, 68, 102690. 10.1016/j.bspc.2021.102690
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. 10.1145/3065386
Kumar, S., Mishra, S., Asthana, P., & Pragya. (2018). Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm (pp. 655–670). 10.1007/978-981-10-3773-3_64
Laosai, J., & Chamnongthai, K. (2018). Classification of acute leukemia using medical-knowledge-based morphology and CD marker. Biomedical Signal Processing and Control, 44, 127–137. 10.1016/j.bspc.2018.01.020
Mahmood, N., Shahid, S., Bakhshi, T., Riaz, S., Ghufran, H., & Yaqoob, M. (n.d.). Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach. 10.1007/s11517-020-02245-2/Published
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2063–2079. 10.1109/TNNLS.2018.2790388
Mirmohammadi, P., Ameri, M., & Shalbaf, A. (2021). Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier. Physical and Engineering Sciences in Medicine, 44(2), 433–441. 10.1007/s13246-021-00993-5
Mishra, S., Majhi, B., & Sa, P. K. (2018). GLRLM-Based Feature Extraction for Acute Lymphoblastic Leukemia (ALL) Detection (pp. 399–407). 10.1007/978-981-10-8636-6_41
Mishra, S., Majhi, B., & Sa, P. K. (2019). Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomedical Signal Processing and Control, 47, 303–311. 10.1016/j.bspc.2018.08.012
Mishra, S., Majhi, B., Sa, P. K., & Sharma, L. (2017) Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomedical Signal Processing and Control, 33, 272–280. 10.1016/j.bspc.2016.11.021
Moraes, L. O., Pedreira, C. E., Barrena, S., Lopez, A., & Orfao, A. (2019). A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas. Computer Methods and Programs in Biomedicine, 178, 85–90. 10.1016/j.cmpb.2019.06.014
Moshavash, Z., Danyali, H., & Helfroush, M. S. (2018). An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images. Journal of Digital Imaging, 31(5), 702–717. 10.1007/s10278-018-0074-y
Mustaqim, T., Fatichah, C., & Suciati, N. (2023). Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review. IEEE Access, 11, 16108–16127. 10.1109/ACCESS.2023.3245128
Negm, A. S., Hassan, O. A., & Kandil, A. H. (2018). A decision support system for Acute Leukaemia classification based on digital microscopic images. Alexandria Engineering Journal, 57(4), 2319–2332. 10.1016/j.aej.2017.08.025
Ouyang, N., Wang, W., Ma, L., Wang, Y., Chen, Q., Yang, S., Xie, J., Su, S., Cheng, Y., Cheng, Q., Zheng, L., & Yuan, Y. (2021). Diagnosing acute promyelocytic leukemia by using convolutional neural network. Clinica Chimica Acta, 512, 1–6. 10.1016/j.cca.2020.10.039
Rawat, J., Singh, A., Bhadauria, H. S., Virmani, J., & Devgun, J. S. (2017). Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimedia Tools and Applications, 76(18), 19057–19085. 10.1007/s11042-017-4478-3
Rehman, A., Abbas, N., Saba, T., Rahman, S. I. ur, Mehmood, Z., & Kolivand, H. (2018). Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique, 81(11), 1310–1317. 10.1002/jemt.23139
Rezayi, S., Ghazisaeedi, M., Kalhori, S. R. N., & Saeedi, S. (2022). Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19. Journal of Medical Signals & Sensors, 12(3), 233–253.
Rezayi, S., Mohammadzadeh, N., Bouraghi, H., Saeedi, S., & Mohammadpour, A. (2021). Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods. Computational Intelligence and Neuroscience, 2021, 1–12. 10.1155/2021/5478157
Sampathila, N., Chadaga, K., Goswami, N., Chadaga, R. P., Pandya, M., Prabhu, S., Bairy, M. G., Katta, S. S., Bhat, D., & Upadya, S. P. (2022). Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images. Healthcare, 10(10), 1812. 10.3390/healthcare10101812
Santhosh Krishna, B. V., Jijin Godwin, J., Tharanee Shree, S., Sreenidhi, B., & Abinaya, T. (2021). Detection of Leukemia and Its Types Using Combination of Support Vector Machine and K-Nearest Neighbors Algorithm (pp. 435–444). 10.1007/978-981-16-0666-3_35
Shafique, S., & Tehsin, S. (2018). Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technology in Cancer Research and Treatment, 17, 1–7. 10.1177/1533033818802789
Shah, S., Nawaz, W., Jalil, B., & Khan, H. A. (2019). Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks (pp. 23–31). 10.1007/978-981-15-0798-4_3
Shahin, A. I., Guo, Y., Amin, K. M., & Sharawi, A. A. (2019). White blood cells identification system based on convolutional deep neural learning networks. Computer Methods and Programs in Biomedicine, 168, 69–80. 10.1016/j.cmpb.2017.11.015
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (n.d.). Going Deeper with Convolutions.
Vogado, L. H. S., Veras, R. D. M. S., Andrade, A. R., de Araujo, F. H. D., Silva, R. R. V., & Aires, K. R. T. (2017). Diagnosing Leukemia in Blood Smear Images Using an Ensemble of Classifiers and Pre-Trained Convolutional Neural Networks. 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 367–373. 10.1109/SIBGRAPI.2017.55
Vogado, L. H. S., Veras, R. M. S., Araujo, Flavio. H. D., Silva, R. R. V., & Aires, K. R. T. (2018). Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Engineering Applications of Artificial Intelligence, 72, 415–422. 10.1016/j.engappai.2018.04.024
Wan, S., Lan, Y., Xu, J., Guo, J., Pang, L., & Cheng, X. (2016). Match-srnn: Modeling the recursive matching structure with spatial rnn.
Wang, S., & He, G. (2016). 2016 Revision to the WHO classification of acute lymphoblastic leukemia. Journal of Translational Internal Medicine, 4(4), 147–149. 10.1515/jtim-2016-0040
Yu, W., Chang, J., Yang, C., Zhang, L., Shen, H., Xia, Y., & Sha, J. (2017). Automatic classification of leukocytes using deep neural network. 2017 IEEE 12th International Conference on ASIC (ASICON), 1041–1044. 10.1109/ASICON.2017.8252657
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7), 1235–1270. 10.1162/neco_a_01199
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Transactions on Image Processing, 26(7), 3142–3155. 10.1109/TIP.2017.2662206
Acharya, V., & Kumar, P. (2019). Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Medical & Biological Engineering & Computing, 57(8), 1783–1811. 10.1007/s11517-019-01984-1
Aftab, M. O., Javed Awan, M., Khalid, S., Javed, R., & Shabir, H. (2021). Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA), 216–220. 10.1109/CAIDA51941.2021.9425264
Akter Hossain, M., Islam Sabik, M., Muntasir, I., Muzahidul Islam, A. K. M., Islam, S., & Ahmed, A. (2020). Leukemia Detection Mechanism through Microscopic Image and ML Techniques. 2020 IEEE REGION 10 CONFERENCE (TENCON), 61–66. 10.1109/TENCON50793.2020.9293925
Alam, A., & Anwar, S. (2021). Detecting Acute Lymphoblastic Leukemia Through Microscopic Blood Images Using CNN (pp. 207–214). 10.1007/978-981-33-6393-9_22
AL-Dulaimi, K., Banks, J., Nugyen, K., Al-Sabaawi, A., Tomeo-Reyes, I., & Chandran, V. (2021). Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review–Challenges, Current and Future Potential Techniques. IEEE Reviews in Biomedical Engineering, 14, 290–306. 10.1109/RBME.2020.3004639
Al-Qudah, R., & Suen, C. Y. (2020). Synthetic Blood Smears Generation Using Locality Sensitive Hashing and Deep Neural Networks. IEEE Access, 8, 102530–102539. 10.1109/ACCESS.2020.2999349
Alsalem, M. A., Zaidan, A. A., Zaidan, B. B., Hashim, M., Madhloom, H. T., Azeez, N. D., & Alsyisuf, S. (2018). A review of the automated detection and classification of acute leukaemia: Coherent taxonomy, datasets, validation and performance measurements, motivation, open challenges and recommendations. Computer Methods and Programs in Biomedicine, 158, 93–112. 10.1016/j.cmpb.2018.02.005
Anilkumar, K. K., Manoj, V. J., & Sagi, T. M. (2022). Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning. IRBM, 43(5), 405–413. 10.1016/j.irbm.2021.05.005
Bhuiyan, Md. N. Q., Rahut, S. K., Tanvir, R. A., & Ripon, S. (2019). Automatic Acute Lymphoblastic Leukemia Detection and Comparative Analysis from Images. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT), 1144–1149. 10.1109/CoDIT.2019.8820299
Bibi, N., Sikandar, M., Ud Din, I., Almogren, A., & Ali, S. (2020). IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning. Journal of Healthcare Engineering, 2020, 1–12. 10.1155/2020/6648574
Bukhari, M., Yasmin, S., Sammad, S., & Abd El-Latif, A. A. (2022). A Deep Learning Framework for Leukemia Cancer Detection in Microscopic Blood Samples Using Squeeze and Excitation Learning. Mathematical Problems in Engineering, 2022, 1–18. 10.1155/2022/2801227
Chen, E., Liao, R., Shalaginov, M. Y., & Zeng, T. H. (2022). Real-time Detection of Acute Lymphoblastic Leukemia Cells Using Deep Learning. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 3788–3790. 10.1109/BIBM55620.2022.9995131
Das, P. K., & Meher, S. (2021a). An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia. Expert Systems with Applications, 183, 115311. 10.1016/j.eswa.2021.115311
Das, P. K., & Meher, S. (2021b). Transfer Learning-Based Automatic Detection of Acute Lymphocytic Leukemia. 2021 National Conference on Communications (NCC), 1–6. 10.1109/NCC52529.2021.9530010
Das, P. K., Meher, S., Panda, R., & Abraham, A. (2022a). A Systematic Review on Recent Advancements in Deep and Machine Learning Based Detection and Classification of Acute Lymphoblastic Leukemia. IEEE Access, 10, 81741–81763. 10.1109/ACCESS.2022.3196037
Das, P. K., Meher, S., Panda, R., & Abraham, A. (2022b). An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders. IEEE Transactions on Cybernetics, 52(10), 10615–10626. 10.1109/TCYB.2021.3062152
Das, P. K., Nayak, B., & Meher, S. (2022c). A lightweight deep learning system for automatic detection of blood cancer. Measurement, 191, 110762. 10.1016/j.measurement.2022.110762
Dese, K., Raj, H., Ayana, G., Yemane, T., Adissu, W., Krishnamoorthy, J., & Kwa, T. (2021). Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images. Clinical Lymphoma Myeloma and Leukemia, 21(11), e903–e914. 10.1016/j.clml.2021.06.025
Genovese, A., Hosseini, M. S., Piuri, V., Plataniotis, K. N., & Scotti, F. (2021a). Acute Lymphoblastic Leukemia Detection Based on Adaptive Unsharpening and Deep Learning. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1205–1209. 10.1109/ICASSP39728.2021.9414362
Genovese, G., Moltrasio, C., Berti, E., & Marzano, A. V. (2021b). Skin manifestations associated with COVID-19: current knowledge and future perspectives. Dermatology, 237(1), 1–12.
Genovese, A., Siegel, M., Diván, M., Piuri, V., Scotti, F., & János Viharos, Z. (2021c). Phone e-mail address TC assignments (joining year) TC mission-field of expertise (max. 1000 char. Including spaces).
Gupta, R., Gehlot, S., & Gupta, A. (2022). C-NMC: B-lineage acute lymphoblastic leukaemia: A blood cancer dataset. Medical Engineering & Physics, 103, 103793. 10.1016/j.medengphy.2022.103793
Hazarika, R. A., Abraham, A., Kandar, D., & Maji, A. K. (2021). An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images. IEEE Access, 9, 161194–161207. 10.1109/ACCESS.2021.3131741
Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2021). Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Systems with Applications, 167, 114161. 10.1016/j.eswa.2020.114161
Jha, K. K., & Dutta, H. S. (2019). Mutual Information based hybrid model and deep learning for Acute Lymphocytic Leukemia detection in single cell blood smear images. Computer Methods and Programs in Biomedicine, 179, 104987. 10.1016/j.cmpb.2019.104987
Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019). A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification. 2019 International Conference on Information and Communication Technology Convergence (ICTC), 271–276. 10.1109/ICTC46691.2019.8939959
Khandekar, R., Shastry, P., Jaishankar, S., Faust, O., & Sampathila, N. (2021). Automated blast cell detection for Acute Lymphoblastic Leukemia diagnosis. Biomedical Signal Processing and Control, 68, 102690. 10.1016/j.bspc.2021.102690
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. 10.1145/3065386
Kumar, S., Mishra, S., Asthana, P., & Pragya. (2018). Automated Detection of Acute Leukemia Using K-mean Clustering Algorithm (pp. 655–670). 10.1007/978-981-10-3773-3_64
Laosai, J., & Chamnongthai, K. (2018). Classification of acute leukemia using medical-knowledge-based morphology and CD marker. Biomedical Signal Processing and Control, 44, 127–137. 10.1016/j.bspc.2018.01.020
Mahmood, N., Shahid, S., Bakhshi, T., Riaz, S., Ghufran, H., & Yaqoob, M. (n.d.). Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach. 10.1007/s11517-020-02245-2/Published
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018). Applications of Deep Learning and Reinforcement Learning to Biological Data. IEEE Transactions on Neural Networks and Learning Systems, 29(6), 2063–2079. 10.1109/TNNLS.2018.2790388
Mirmohammadi, P., Ameri, M., & Shalbaf, A. (2021). Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier. Physical and Engineering Sciences in Medicine, 44(2), 433–441. 10.1007/s13246-021-00993-5
Mishra, S., Majhi, B., & Sa, P. K. (2018). GLRLM-Based Feature Extraction for Acute Lymphoblastic Leukemia (ALL) Detection (pp. 399–407). 10.1007/978-981-10-8636-6_41
Mishra, S., Majhi, B., & Sa, P. K. (2019). Texture feature based classification on microscopic blood smear for acute lymphoblastic leukemia detection. Biomedical Signal Processing and Control, 47, 303–311. 10.1016/j.bspc.2018.08.012
Mishra, S., Majhi, B., Sa, P. K., & Sharma, L. (2017) Gray level co-occurrence matrix and random forest based acute lymphoblastic leukemia detection. Biomedical Signal Processing and Control, 33, 272–280. 10.1016/j.bspc.2016.11.021
Moraes, L. O., Pedreira, C. E., Barrena, S., Lopez, A., & Orfao, A. (2019). A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas. Computer Methods and Programs in Biomedicine, 178, 85–90. 10.1016/j.cmpb.2019.06.014
Moshavash, Z., Danyali, H., & Helfroush, M. S. (2018). An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images. Journal of Digital Imaging, 31(5), 702–717. 10.1007/s10278-018-0074-y
Mustaqim, T., Fatichah, C., & Suciati, N. (2023). Deep Learning for the Detection of Acute Lymphoblastic Leukemia Subtypes on Microscopic Images: A Systematic Literature Review. IEEE Access, 11, 16108–16127. 10.1109/ACCESS.2023.3245128
Negm, A. S., Hassan, O. A., & Kandil, A. H. (2018). A decision support system for Acute Leukaemia classification based on digital microscopic images. Alexandria Engineering Journal, 57(4), 2319–2332. 10.1016/j.aej.2017.08.025
Ouyang, N., Wang, W., Ma, L., Wang, Y., Chen, Q., Yang, S., Xie, J., Su, S., Cheng, Y., Cheng, Q., Zheng, L., & Yuan, Y. (2021). Diagnosing acute promyelocytic leukemia by using convolutional neural network. Clinica Chimica Acta, 512, 1–6. 10.1016/j.cca.2020.10.039
Rawat, J., Singh, A., Bhadauria, H. S., Virmani, J., & Devgun, J. S. (2017). Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers. Multimedia Tools and Applications, 76(18), 19057–19085. 10.1007/s11042-017-4478-3
Rehman, A., Abbas, N., Saba, T., Rahman, S. I. ur, Mehmood, Z., & Kolivand, H. (2018). Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique, 81(11), 1310–1317. 10.1002/jemt.23139
Rezayi, S., Ghazisaeedi, M., Kalhori, S. R. N., & Saeedi, S. (2022). Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19. Journal of Medical Signals & Sensors, 12(3), 233–253.
Rezayi, S., Mohammadzadeh, N., Bouraghi, H., Saeedi, S., & Mohammadpour, A. (2021). Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence-Oriented Deep Learning Methods. Computational Intelligence and Neuroscience, 2021, 1–12. 10.1155/2021/5478157
Sampathila, N., Chadaga, K., Goswami, N., Chadaga, R. P., Pandya, M., Prabhu, S., Bairy, M. G., Katta, S. S., Bhat, D., & Upadya, S. P. (2022). Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images. Healthcare, 10(10), 1812. 10.3390/healthcare10101812
Santhosh Krishna, B. V., Jijin Godwin, J., Tharanee Shree, S., Sreenidhi, B., & Abinaya, T. (2021). Detection of Leukemia and Its Types Using Combination of Support Vector Machine and K-Nearest Neighbors Algorithm (pp. 435–444). 10.1007/978-981-16-0666-3_35
Shafique, S., & Tehsin, S. (2018). Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technology in Cancer Research and Treatment, 17, 1–7. 10.1177/1533033818802789
Shah, S., Nawaz, W., Jalil, B., & Khan, H. A. (2019). Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks (pp. 23–31). 10.1007/978-981-15-0798-4_3
Shahin, A. I., Guo, Y., Amin, K. M., & Sharawi, A. A. (2019). White blood cells identification system based on convolutional deep neural learning networks. Computer Methods and Programs in Biomedicine, 168, 69–80. 10.1016/j.cmpb.2017.11.015
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (n.d.). Going Deeper with Convolutions.
Vogado, L. H. S., Veras, R. D. M. S., Andrade, A. R., de Araujo, F. H. D., Silva, R. R. V., & Aires, K. R. T. (2017). Diagnosing Leukemia in Blood Smear Images Using an Ensemble of Classifiers and Pre-Trained Convolutional Neural Networks. 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 367–373. 10.1109/SIBGRAPI.2017.55
Vogado, L. H. S., Veras, R. M. S., Araujo, Flavio. H. D., Silva, R. R. V., & Aires, K. R. T. (2018). Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification. Engineering Applications of Artificial Intelligence, 72, 415–422. 10.1016/j.engappai.2018.04.024
Wan, S., Lan, Y., Xu, J., Guo, J., Pang, L., & Cheng, X. (2016). Match-srnn: Modeling the recursive matching structure with spatial rnn.
Wang, S., & He, G. (2016). 2016 Revision to the WHO classification of acute lymphoblastic leukemia. Journal of Translational Internal Medicine, 4(4), 147–149. 10.1515/jtim-2016-0040
Yu, W., Chang, J., Yang, C., Zhang, L., Shen, H., Xia, Y., & Sha, J. (2017). Automatic classification of leukocytes using deep neural network. 2017 IEEE 12th International Conference on ASIC (ASICON), 1041–1044. 10.1109/ASICON.2017.8252657
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019). A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures. Neural Computation, 31(7), 1235–1270. 10.1162/neco_a_01199
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Transactions on Image Processing, 26(7), 3142–3155. 10.1109/TIP.2017.2662206
Faiz, M., Gari Mounika, B., Akbar, M., & Srivastava, S. (2024). Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31420. https://doi.org/10.14201/adcaij.31420
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