An Efficient System for Detection and Classification of Acute Lymphoblastic Leukemia Using Semi-Supervised Segmentation Technique

Acute lymphoblastic leukemia (ALL), sometimes referred to as hematopoietic cancer or blood cancer, is a group of cancers that impact lymphocytes, which are white blood cells. Improving patient outcomes and developing efficient treatment plans depend on early and precise blood cancer diagnosis. The lack of labelled data makes it difficult to segment lymphoblast cells from microscopic images. Our research aimed to achieve unsupervised approach for precise and accurate segmentation of blasted lymphocyte cells, thereby improving the overall performance of ALL detection and classification into its subtypes L1, L2 and L3. The proposed method employs k-means segmentation, where the parameter k is tuned, and optimal value is determined based on segmentation quality. For better performance, generated segments are evaluated against ground truth image based on Structural Similarity Index Measure (SSIM), Dice similarity coefficient (DSC) and Intersection over union (IoU). The algorithm iterates over different values of k, assesses the segmentation quality, and selects the segment with the highest evaluation score. Customized convolutional neural networks are employed for categorization. The data augmentation technique has been applied to expand the amount of training data in order to enhance model efficiency. The ALL-IDB dataset is used to assess the model's performance, and the experimental results showed that the suggested method can identify blasted cell subtypes with an overall accuracy of 99%. We succeeded in detecting acute lymphoblastic leukemia with 100% accuracy. Our proposed model not only enhances the accuracy significantly but also determines the optimal value of clusters (k) for more effective segmentation.