Early Blood Cancer Detection Using Lightweight Deep Learning: Classifying Microscopic Images Using MobileNetV2

Because of its delicate cellular manifestations, blood cancer presents a substantial difficulty in clinical diagnostics, especially in its early stages. Deep learning methods for automated detection can improve early diagnosis and patient outcomes. A deep learning method based on MobileNetV2 is presented in this work for the early identification of blood cancer from photographs of tiny blood cells. To improve classification accuracy while preserving computational economy, the suggested model makes use of transfer learning and data augmentation approaches. The labelled microscopic pictures in the collection are divided into several stages, such as benign, early pre-B, pre-B, and pro-B. Standard performance indicators including accuracy, precision, recall, and F1-score are used to train and assess the model. According to experimental results, MobileNetV2 can achieve high classification accuracy at low computing cost, which makes it appropriate for real-time clinical applications. The results imply that automated detection based on deep learning may be a scalable and effective technique to help haematologists diagnose blood cancer in its early stages.