Convolutional Neural Network-Based Automated Acute Lymphoblastic Leukaemia Detection and Stage Classification from Peripheral Blood Smear Images
Keywords:
Medical Image Analysis, Acute Lymphoblastic Leukaemia, Convolutional Neural Network,, Deep Learning, Blood Smear ImagesAbstract
Acute Lymphoblastic Leukaemia (ALL) poses significant challenges in diagnosis and treatment due to its high mortality rates and complex subtyping. In this study, ALLNet, a Convolutional Neural Networks (CNN) is employed to automate ALL detection using a publicly available dataset of microscopic peripheral blood smear (PBS) images. The ALLNet model demonstrates good performance, achieving an accuracy of 92% after 70 epochs of training. Through extensive evaluation using classification reports and confusion matrices, the model`s ability to differentiate between four classes of Benign, Early, Pre, and Pro have been analysed. The accuracy, recall, precision, and F1-score metrics for each class indicate robust performance, with particularly high values for the `Pro` class, suggesting the model`s efficacy in capturing nuanced patterns indicative of different leukaemia subtypes. Furthermore, the investigation highlights ALLNet‘s consistency across the dataset, effectively minimising both false positives and false negatives. These findings underscore the potential of CNNs in medical image analysis, particularly in the domain of leukaemia classification and detection.
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