A new lightweight convolutional neural network for radiation-induced liver disease classification

• Published in: Biomedical signal processing and control Vol. 73; p. 103463
• Main Authors: Alici-Karaca, Demet, Akay, Bahriye, Yay, Arzu, Suna, Pinar, Nalbantoglu, O. Ufuk, Karaboga, Dervis, Basturk, Alper, Balcioglu, Esra, Baran, Munevver
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-03-2022
• Subjects: Deep learning; Histopathology; Image classification; RILD dataset; Deep learning; Histopathology; RILD dataset; Image classification
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.103463

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•This article is the first to classify RILD histopathological images using deep learning.•A lightweight CNN model is designed for the classification of damages in liver images.•An accuracy of 100% for binary classification and an accuracy of 87.57% for multi-class classification has been achieved.•The novel architecture aims to improve diagnostic accuracy.•The proposed approach can be helpful to reduce the workload of pathologists.
Histopathological image analysis is used in the diagnosis of many diseases such as cancer, brain tumor, fatty liver and congenital heart diseases. In clinical practice, the time-consuming diagnostic process requires an expertise in the field, and disagreements can arise among pathologists at the decision stage. Machine learning methods can be used in diagnostic stage to overcome these issues. Especially, the use of deep learning systems in the analysis of histopathological images can reduce the workload of pathologists and can provide more objective results. Over traditional machine learning methods using handcrafted features, deep learning approaches that can automatically learn features stand out with their performance in the analysis of histopathology images. In this paper, we propose a patch-based lightweight convolution neural network for histopathological image classification for detecting radiation-induced liver damage. The proposed model was trained to classify the radiation-induced liver disease dataset comprising 555 histopathological images. An accuracy of 100% for binary classification and an accuracy of 87.57% for multi-class classification were achieved on the unseen test set. Our model outperforms state-of-the-art models, including ResNet-50, AlexNet, Vgg16, and GoogleNet. This paper is the first to classify radiation-induced liver disease data with deep learning, and it can be a promising tool for diagnosis purpose of pathologists. Since it is vital to detect a damage that leads to deteriorating liver function, this study is valuable in the field of medicine.