Leukemia Cancer Classification using Micro GeneArray Dataset

Abstract

Leukemia, a heterogeneous group of hematologic malignancies, poses significant challenges in diagnosis and treatment due to its diverse genetic and molecular characteristics. This thesis explores the application of deep learning techniques to classify leukemia subtypes based on gene expression profiles. Utilizing datasets sourced from the Gene Expression Omnibus database, this study implements and evaluates three deep learning models: Long Short-Term Memory, Bidirectional LSTM, and a novel architecture termed xLSTM, which incorporates custom layers and residual connections for enhanced performance. Data preprocessing involved standardizing datasets, imputing missing values, and selecting the top 400 features through ChiSquared testing. Models were trained on these processed datasets, and their performance was assessed through metrics such as accuracy and loss. The xLSTM model demonstrated superior performance, achieving a final test accuracy of 98.18%, outperforming both LSTM and BiLSTM models. Furthermore, the study examines the integration of multimodal data, combining gene expression profiles with images data to enhance classification accuracy. Recommendations for researchers emphasize the importance of high-quality, diverse datasets and advanced preprocessing techniques. Clinicians are encouraged to adopt deep learning tools in practice and participate in data sharing initiatives, while policymakers are urged to support funding, standardization, and ethical guidelines in AI applications. This research underscores the transformative potential of deep learning in medical diagnostics, advocating for continued innovation and collaboration across the biomedical and AI research communities to enhance patient outcomes in leukemia treatment.