SwiftCNN: A Deep Learning Model for B-ALL Diagnosis and Subtype Classification from Blood Smear Images

The most advanced methods available today are convolutional neural networks (CNNs), which are frequently used for image categorization tasks. This article uses sophisticated neural network models to explore the categorization of peripheral blood smear pictures for B-ALL diagnosis and its subtypes. We introduce a method for classifying images using a modified VGG19 model. Images are pre-processed at first before being input into the multi-class classification algorithms. We discovered throughout this study that the suggested methods improve model performance. Our study focused on two types of images: benign and malignant, as well as three subtypes of malignant lymphoblasts: Early Pre-B, Pre-B, and Pro-B ALL. The model has a validation loss of 0.1499 and an accuracy of 94.63%, whereas its training loss is 0.1127 and 96.97%, respectively. These findings demonstrate how well the VGG19-based model performs in terms of categorization.