Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2023Scopus SJRQ1PatchResNet: Multiple Patch Division–Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images
Muezzinoglu, Taha; Baygin, Nursena; Tuncer, Ilknur; Barua, Prabal Datta; Baygin, Mehmet; Dogan, Sengul; Tuncer, Turker; Palmer, Elizabeth Emma; Cheong, Kang Hao; Acharya, U. Rajendra
Modern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have been used with CNN for image classification. Thus, automated brain tumor classification models have been proposed by deploying CNNs to help medical professionals. Our primary objective is to increase the classification performance using CNN. Therefore, a patch-based deep feature engineering model has been proposed in this work. Nowadays, patch division techniques have been used to attain high classification performance, and variable-sized patches have achieved good results. In this work, we have used three types of patches of different sizes (32 × 32, 56 × 56, 112 × 112). Six feature vectors have been obtained using these patches and two layers of the pretrained ResNet50 (global average pooling and fully connected layers). In the feature selection phase, three selectors—neighborhood component analysis (NCA), Chi2, and ReliefF—have been used, and 18 final feature vectors have been obtained. By deploying k nearest neighbors (kNN), 18 results have been calculated. Iterative hard majority voting (IHMV) has been applied to compute the general classification accuracy of this framework. This model uses different patches, feature extractors (two layers of the ResNet50 have been utilized as feature extractors), and selectors, making this a framework that we have named PatchResNet. A public brain image dataset containing four classes (glioblastoma multiforme (GBM), meningioma, pituitary tumor, healthy) has been used to develop the proposed PatchResNet model. Our proposed PatchResNet attained 98.10% classification accuracy using the public brain tumor image dataset. The developed PatchResNet model obtained high classification accuracy and has the advantage of being a self-organized framework. Therefore, the proposed method can choose the best result validation prediction vectors and achieve high image classification performance.
AtıfScopus103OpenAlex101
İncele - MakaleAçık Erişim2023Scopus SJRQ2Application of the deep transfer learning framework for hydatid cyst classification using CT images
Gul, Yeliz; Muezzinoglu, Taha; Kilicarslan, Gulhan; Dogan, Sengul; Tuncer, Turker
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.
AtıfScopus10WoS5OpenAlex8İncele