Deep Learning for Leukemia Classification: Performance Analysis and Challenges Across Multiple Architectures

Leukemia is a very heterogeneous and complex blood cancer, which poses a significant challenge in its proper categorization and diagnosis. This paper aims to introduce various deep learning architectures, namely EfficientNet, LeNet, AlexNet, ResNet, VGG, and custom CNNs, for improved classification of leukemia subtypes. These models provide much improvement in feature extraction and learning, which further helps in the performance and reliability of classification. A web-based interface has also been provided through which a user can upload images and clinical data for analysis. The interface displays model predictions, symptom analysis, and accuracy metrics. Data collection, preprocessing, normalization, and scaling are part of the framework, considering leukemia cell images, genomic features, and clinical records. Using the preprocessed data, training is performed on the various models with thorough testing and validation to fine-tune the best-performing architecture. Among these, AlexNet gave a classification accuracy of 88.975%. These results strongly underscore the potential of advanced deep learning techniques to radically transform leukemia diagnosis and classification for precision medicine.