A lightweight deep learning system for automatic detection of blood cancer. (15th March 2022)

• Record Type: Journal Article
• Authors: Das, Pradeep Kumar; Nayak, Biswajit; Meher, Sukadev
• Is Part Of: Measurement. Volume 191(2022)
• Publication Date: 2022-03-15
• Subjects: Acute lymphoblastic leukemia -- Acute myeloid leukemia -- Classification -- Deep learning -- Detection -- Transfer learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.110762
• Languages: English
• ISSNs: 0263-2241

Abstract

Abstract: Microscopic analysis of blood-cells is an essential and vital task for the early diagnosis of life-threatening hematological disorders like blood cancer (leukemia). We have presented an effective and computationally efficient approach for automatically detecting and classifying Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML). Currently, transfer learning has succeeded as a preferred approach in medical image analysis since it achieves excellent performance in a small database. This paper proposes a lightweight transfer-learning-based feature extraction followed by Support Vector Machine (SVM)-based classification technique for efficient ALL and AML detection. It yields a faster and more efficient system due to the depth-wise separable convolution, tunable multiplier, and inverted residual bottleneck structure. Moreover, the SVM-based classification improves the overall performance by optimizing the hyperplane location. Furthermore, the experimental results signify that our proposed system gains superior performance than others in all these three publicly available standard ALLIDB1, ALLIDB2, and ASH databases. Highlights: A computationally efficient blood cancer detection system is proposed. Lightweight deep learning-based feature extraction is suggested to detect Leukemia. Depth-wise separable convolution and inverted residual bottleneck makes it faster. SVM-based classification boosts the performance by optimizing hyperplane location. Experimental results signify the superiority of proposed deep learning model.