An efficient deep Convolutional Neural Network based detection and classification of Acute Lymphoblastic Leukemia

Automated and accurate diagnosis of Acute Lymphoblastic Leukemia (ALL), blood cancer, is a challenging task. Nowadays, Convolutional Neural Networks (CNNs) have become a preferred approach for medical image analysis. However, for achieving excellent performance, classical CNNs usually require huge databases for proper training. This paper proposes an efficient deep CNNs framework to mitigate this issue and yield more accurate ALL detection. The salient features: depthwise separable convolutions, linear bottleneck architecture, inverted residual, and skip connections make it a faster and preferred approach. In this proposed method, a novel probability-based weight factor is suggested, which has a significant role in efficiently hybridizing MobilenetV2 and ResNet18 with preserving the benefits of both approaches. Its performance is validated using public benchmark datasets: ALLIDB1 and ALLIDB2. The experimental results display that the proposed approach yields the best accuracy (with 70% training and 30% testing) 99.39% and 97.18% in ALLIDB1 and ALLIDB2 datasets, respectively. Similarly, it also achieves the best accuracy (with 50% training and 50% testing) 97.92% and 96.00% in ALLIDB1 and ALLIDB2 datasets, respectively. Moreover, it also achieves the best performance compared to the recent transfer learning-based techniques in both the datasets, in terms of sensitivity, specificity, accuracy, precision, F1 score, and receiver operating characteristic (ROC) in most of the cases.

Automated summary

This paper proposes an efficient deep CNN framework for the diagnosis of Acute Lymphoblastic Leukemia (ALL), which utilizes a novel probability-based weight factor, resulting in improved performance. The proposed method is validated on public benchmark datasets, demonstrating the best accuracy in comparison to recent transfer learning-based techniques.