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Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection

Toğaçar Mesut, Ergen Burhan, Tümen Vedat

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 2

646--666

Retinal disease is one of the diseases that cause visual symptoms or loss of vision in humans. This disease can affect the choroid, which severely affects vision. Optical coherence tomography (OCT) images are usually used to detect retinal disease. OCT is an imaging technique that takes high-resolution slices of retinal images. It takes time for experts to examine and interpret the OCT images. Experts need to take advantage of technological capabilities to make this process faster and more accurate. Three datasets were used in this study. Dataset #1 (UCSD dataset) consists of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal OCT image types. Dataset #2 (Duke dataset) and Dataset #3 consist of age-related macular degeneration (AMD), DME, and normal OCT image types. An artificial intelligence based hybrid approach was proposed for retinal disease detection. In the proposed approach, class-based activations were extracted for each model with nine transfer learning models using the dataset. Next, the dominant activations were selected from the model-based activations of each class using the slime mold algorithm (SMA) and the selected activations were classified using the softmax method. The overall accuracy obtained in classification is as follows: 99.60% for dataset 1, 99.89% for dataset #2 and 97.49% for dataset #3. In this study, it was found that the proposed approach contributes to the performance of transfer learning models.

Detection of lung cancer on chest CT images using minimum redundancy maximum relevance feature selection method with convolutional neural networks

Togaçar Mesut, Ergen Burhan, Cömert Zafer

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

23--39

Lung cancer is a disease caused by the involuntary increase of cells in the lung tissue. Early detection of cancerous cells is of vital importance in the lungs providing oxygen to the human body and excretion of carbon dioxide in the body as a result of vital activities. In this study, the detection of lung cancers is realized using LeNet, AlexNet and VGG-16 deep learning models. The experiments were carried out on an open dataset composed of Computed Tomography (CT) images. In the experiment, convolutional neural networks (CNNs) were used for feature extraction and classification purposes. In order to increase the success rate of the classification, the image augmentation techniques, such as cutting, zooming, horizontal turning and filling, were applied to the dataset during the training of the models. Because of the outstanding success of AlexNet model, the features obtained from the last fully-connected layer of the model were separately applied as the input to linear regression (LR), linear discriminant analysis (LDA), decision tree (DT), support vector ma-chine (SVM), k -nearest neighbor (kNN) and softmax classifiers. A combination of AlexNet model and k NN classifier achieved the most efficient classification accuracy as 98.74 %. Then, the minimum redundancy maximum relevance (mRMR) feature selection method was applied to the deep feature set to choose the most efficient features. Consequently, the success rate was yielded as 99.51 % by reclassifying the dataset with the selected features and k NN model. The proposed model is consistent diagnosis model for lung cancer detection using chest CT images.