Acute-Lymphocytic-Leukemia-ALL-Cell-Classification

Due to morphological similarity at the microscopic level, making an accurate and time sensitive distinction between blood cells affected by Acute Lymphocytic Leukemia (ALL) and their healthy counterparts calls for the usage of machine learning architectures. However, three of the most common models, VGG, ResNet, and Inception, each come with their own set of flaws with room for improvement which demands the need for a superior model. ALLNet, the proposed hybrid convolutional neural network architecture, consists of a combination of the VGG, ResNet, and Inception models. The ALL Challenge dataset of ISBI 2019 contains 10,691 images of white blood cells which were used to train and test the models, 7,272 of cells with ALL and 3,419 of those that were healthy. Of the images, 60% was used to train the model, 20% for the cross validation set, and 20% for the test set. ALLNet outperformed the VGG, Resnet, and the Inception models across the board, achieving an accuracy of 92.6567%, a sensitivity of 95.5304%, a specificity of 85.9155%, an AUC score of 0.966347, and an F1 score of 0.94803 in the cross validation set. In the test set, the ALLNet achieved an accuracy of 92.0991%, a sensitivity of 96.5446%, a specificity of 82.8035%, an AUC score of 0.959972, and an F1 score of 0.942963. The utilization of ALLNet in the clinical workspace can better treat the thousands of people suffering with ALL across the world, many of whom are children.