Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models.
Keywords: Computer-aided diagnostic; Convolutional neural network; Deep learning; Leukemia; Transfer learning models.
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