Adrenal Lesion Classification on T1-Weighted Abdomen Images with Convolutional Neural Networks

Year 2022, Volume: 14 Issue: 3, 268 - 275, 31.12.2022

Ahmet Solak Rahime Ceylan Mustafa Alper Bozkurt Hakan Cebeci Mustafa Koplay

https://doi.org/10.29137/umagd.1215882

Abstract

Adrenal lesions are usually discovered incidentally during other health screenings and are usually benign. However, it is vital to take precautions when a malignant adrenal lesion is detected. Especially deep learning models developed in the last ten years give successful results on medical images. In this paper, adrenal lesion characterization on T1-weighted magnetic resonance abdomen images was aimed using convolutional neural network (CNN) which is one of the deep learning methods. Firstly, effects of important model parameters are assessed on performance of CNN, so optimum CNN model is obtained for classification of adrenal lesions. For a fixed number of convolution filters determined in the first stage of the study, CNN model implemented by different kernel sizes were trained. According to the best result obtained, this time the kernel size was kept constant, and experiments were made for different filter numbers. Finally, studies were carried out with CNN structures of different depths and the results were compared. As a result of the studies, when filter is selected as [5 20], the best results in the trainings conducted with a single-block CNN structure are obtained 0.97, 0.90, 0.98, 0.90, 0.90, and 0.94, for accuracy, sensitivity, specificity, precision, F1-score, and AUC score, respectively. The study was compared with the studies in the literature, and it was seen that it was superior to them.

Keywords

Abdomen, Adrenal Lesion, Classification, Convolutional Neural Network, Deep Learning, Magnetic Resonance

Supporting Institution

Konya Teknik Üniversitesi Öğretim Üyesi Yetiştirme Programı

Project Number

2018-OYP-033

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