Transfer Learning-Based Automatic Detection of Acute Lymphocytic Leukemia

Abstract

In healthcare, microscopic analysis of blood-cells is considered significant in diagnosing acute lymphocytic leukemia (ALL). Manual microscopic analysis is an error-prone and timetaking process. Hence, there is a need for automatic leukemia diagnosis. Transfer learning is becoming an emerging medical image processing technique because of its superior performance in small databases, unlike traditional deep learning techniques. In this paper, we have suggested a new transfer-learning-based automatic ALL detection method. A light-weight, highly computationally efficient ShuffleNet is applied to classify malignant and benign with promising classification performance. Channel shuffling and pointwise-group convolution boost its performance and make it faster. The proposed method is validated on the standard ALLIDB1 and ALLIDB2 databases. The experimental results show that in most cases, the proposed ALL detection model outperforms Xception, NasNetMobile, VGG19, and ResNet50 with promising quantitative performance.