<scp>MHAOSL</scp>‐Net: A Global Context‐Aware Attention Free Transformer Network With Orthogonal <scp>SoftMax</scp> Layer to Detect Subtypes of Acute Lymphoblastic Leukemia

ABSTRACT Recent advancements in Deep Learning (DL) have enabled the development of Computer‐Aided Diagnosis (CAD) systems for detecting Acute Lymphoblastic Leukemia (ALL) and its subtypes. However, this field faces challenges, particularly due to limited annotated datasets and a lack of efficient modalities. To address these issues, we propose MHAOSL‐net, a novel hybrid DL model specifically designed for the accurate classification of B‐ALL, T‐ALL, and normal cells in blood smear images. The key contributions lie in the integration of four primary components: (1) lightweight MobileNetV2 for backbone feature extraction, (2) a Global Context Information Convolutional Block Attention Module (GCI‐CBAM) for refined local representation using contextual information, (3) an Attention‐Free Transformer (AFT) that captures global dependencies replacing traditional self‐attention, and (4) an Orthogonal SoftMax Layer (OSL) that improves class separability by enforcing orthogonality in the decision space. This unified architecture not only reduces computational overhead but also improves classification performance and generalizability. To the best of our knowledge, this is the first framework that combines an AFT with an OSL in the context of leukemia subtype detection. The performance analysis of the proposed 3‐class classification scheme has been assessed on two novel datasets, namely BBCI_B&amp;T_ALL_2024 and heterogeneous datasets. The experimental results show that the proposed scheme provides better performance, with 99.52% accuracy, 99.36% average precision, and 99.36% average F1‐score on the BBCI_B&amp;T_ALL_2024 . Similarly, it achieves better performance with 99.55% accuracy, 99.40% average precision, and 99.40% average F1‐score on the heterogeneous dataset. The qualitative investigation using Gradient‐weighted Class Activation Mapping (Grad‐CAM) visualization also confirms the efficacy of the proposed model for detecting B‐ALL, T‐ALL, and normal cells. The comparative studies establish the superiority of the proposed scheme over other state‐of‐the‐art approaches. These findings indicate that MHAOSL‐Net offers a promising and efficient solution for reliable ALL subtype detection in clinical settings.