14th International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing (CGVCVIP 2020), Zagreb, Croatia, 2020
The aim of this work is to classify Hemangioma, Rosacea and Acne Vulgaris diseases from digital c... more The aim of this work is to classify Hemangioma, Rosacea and Acne Vulgaris diseases from digital colored photographs automatically. To determine the most appropriate deep neural network for this multi-class classification, network architectures have been examined. To perform a meaningful comparison of deep networks, they should be (i) implemented with the same parameters, (ii) applied with the same activation, loss and optimization functions, (iii) trained and tested with the same datasets, (iv) run on computers having the same hardware configurations. Therefore, in this work, five deep networks, which are applied widely in image classification, have been used to compare their performances by considering these factors. Those networks are VGG16, VGG19, GoogleNet, InceptionV3 and ResNet101. Comparative evaluations of the results obtained from these networks have been performed in terms of accuracy, precision and specificity. F1 score and Matthew's correlation coefficient values have also been computed. Experimental results indicated that ResNet101 architecture can classify images used in this study with higher accuracy (77.72%) than the others.
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Papers by Evgin Goceri
There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments have the most impact at the early stage of the disease. Neuroimages obtained by different imaging techniques (for example, diffusion tensor‐based and magnetic resonance‐based imaging) provide powerful information and help to diagnose the disease. In this work, a deeply supervised and robust method has been developed using three dimensional features to provide objective and accurate diagnosis from magnetic resonance images. The main contributions are (a) a new three dimensional convolutional neural network topology; (b) a new Sobolev gradient‐based optimization with weight values for each decision parameters; (c) application of the proposed topology and optimizer to diagnose Alzheimer's disease; (d) comparisons of the results obtained from the recent techniques that have been implemented for
Alzheimer's disease diagnosis. Experimental results and quantitative evaluations indicated that the proposed network model is able to achieve to extract desired features from images and provides automated diagnosis with 98.06% accuracy.
analysis medical images automatically for diagnosis/assessment of a disease. DL enables higher level of abstraction and
provides better prediction from datasets. Therefore, DL has a great impact and become popular in recent years. In this
work, we present advances and future researches on DL based medical image analysis.
There is not an effective cure for the disease. However, early diagnosis has an important role for treatment planning to delay its progression since the treatments have the most impact at the early stage of the disease. Neuroimages obtained by different imaging techniques (for example, diffusion tensor‐based and magnetic resonance‐based imaging) provide powerful information and help to diagnose the disease. In this work, a deeply supervised and robust method has been developed using three dimensional features to provide objective and accurate diagnosis from magnetic resonance images. The main contributions are (a) a new three dimensional convolutional neural network topology; (b) a new Sobolev gradient‐based optimization with weight values for each decision parameters; (c) application of the proposed topology and optimizer to diagnose Alzheimer's disease; (d) comparisons of the results obtained from the recent techniques that have been implemented for
Alzheimer's disease diagnosis. Experimental results and quantitative evaluations indicated that the proposed network model is able to achieve to extract desired features from images and provides automated diagnosis with 98.06% accuracy.
analysis medical images automatically for diagnosis/assessment of a disease. DL enables higher level of abstraction and
provides better prediction from datasets. Therefore, DL has a great impact and become popular in recent years. In this
work, we present advances and future researches on DL based medical image analysis.