Comparison of machine learning and deep learning methods for detection of liver abnormality

Liver is a vital organ that performs various essential functions. Liver cancer can have severe implications on human health, including death. To reduce the mortality rate, early detection of hepatic cancer is imperative. For rapid diagnosis and treatment planning, developing a computerized system that can aid in the diagnosis is essential. In this paper, machine learning and deep learning methods were compared for identifying abnormal liver from computed tomography images. The liver was segmented using DeepLabv3 + network. The machine learning method used, histogram and gray level co-occurrence matrix for feature extraction and support vector machine for classification. The deep learning methods investigated were ResNet, ShuffleNet and EfficientNet. Accuracy, sensitivity and specificity were used for performance evaluation. The results of our experiments showed that the deep learning methods outperformed the machine learning methods. Among the deep learning models, EfficientNet gave the highest accuracy of 87%. As future work, the abnormal liver can be further classified into benign and malignant classes.