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Acute lymphoblastic leukemia (ALL), one of the prevalent types of carcinogenic disease, has been seen a deadly illness exposing numerous patients across the world to potential threats of lives. It impacts both adults and children providing a narrow range of chances of being cured if diagnosed at a later stage. A definitive diagnosis often demands highly invasive diagnostic procedures thereby proving time-consuming and expensive. Peripheral Blood Smear (PBS) images have been playing a crucial role in the initial screening of ALL in suspected individuals. However, the nonspecific nature of ALL poses a substantial challenge in the analysis of these images thus leaving space for misdiagnosis. Aiming at contribute to the early diagnoses of this life-threatening disease, we put forward automated platform for screening the presence of ALL concerning its specific subtypes (benign, Early Pro-B, Pro-B and Pre-B) using PBS images. The proposed web based platform follows weighted ensemble learning technique using a Residual Convolutional Neural Network (ResNet-152) as the base learner to identify ALL from hematogone cases and then determine ALL subtypes. This is likely to save both diagnosis time and the efforts of clinicians and patients. Experimental results are obtained and comparative analysis among 7 well-known CNN Network architectures (AlexNet, VGGNet, Inception, ResNet-50, ResNet-18, Inception and DenseNet-121) is also performed that demonstrated that the proposed platform achieved comparatively high accuracy (99.95%), precision (99.92%), recall (99.92%), F1-Score (99.90%), sensitivity (99.92%) and specificity (99.97%). The promising results demonstrate that the proposed platform has the potential to be used as a reliable tool for early diagnosis of ALL and its sub-types. Furthermore, this provides references for pathologists and healthcare providers, aiding them in producing specific guidelines and more informed choices about patient and disease management.

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A framework for Early Detection of Acute Lymphoblastic Leukemia and its Subtypes from Peripheral Blood Smear Images Using Deep Ensemble Learning Technique