Abstract
Hydatid cyst, which causes important public health problems, is a disease frequently reported by radiologists. Classification of hydatid cyst types is very important to give the most suitable cure to patients. In order to decrease the misclassification rates of these cysts, machine learning is a good solution to create intelligent medical assistants. The primary objective of this work is to develop a new image classification framework to classify hydatid cyst types. Firstly, we collected a computed tomography (CT) image dataset. This CT image dataset consists of 2416 CT images with five classes (hydatid cyst types). We proposed a new deep feature engineering dataset framework for this dataset. This framework consists of (i) deep feature extraction by deploying transfer learning, (ii) iterative feature selection using INCA (iterative neighborhood component analysis), and (iii) classification. In the first phase—the feature extraction phase—we extracted deep features by deploying ten pretrained convolutional neural networks (CNNs). The used CNNs are commonly used CNNs, and the deep features have been extracted using the last pooling and fully connected layers of these networks. INCA has been applied to the generated deep features by each feature vector. k-nearest neighbors (kNN) classifier has been utilized to generate predicted values of these 10 CNNs using the selected features by INCA. Our proposed INCA-based deep feature extractor attained over 92% classification accuracy for each pretrained model. Our proposed framework reached over 92% classification accuracies, clearly demonstrating that our proposed framework is good image classification.
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This research has been approved on ethical grounds by the Non-Interventional Research Ethics Board Decisions, Firat University, on April 7, 2022 (2022/05–23).
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Gul, Y., Muezzinoglu, T., Kilicarslan, G. et al. Application of the deep transfer learning framework for hydatid cyst classification using CT images. Soft Comput 27, 7179–7189 (2023). https://doi.org/10.1007/s00500-023-07945-z
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DOI: https://doi.org/10.1007/s00500-023-07945-z