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All Journal International Journal of Artificial Intelligence
Sadeghi, Faezeh
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Computer Science & IT Decision Sciences, Operations Research & Management

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A Novel Sep-Unet Architecture of Convolutional Neural Networks to Improve Dermoscopic Image Segmentation by Training Parameters Reduction Sadeghi, Faezeh; Taheri, Mohammad; Rastgarpour, Maryam; Sharifi, Arash
International Journal of Artificial Intelligence Vol 9 No 2: December 2022
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-0902.405

Abstract

Nowadays, we use dermoscopic images as one of the imaging methods in diagnosis of skin lesions such as skin cancer. But due to the noise and other problems, including hair artifacts around the lesion, this issue requires automatic and reliable segmentation methods. The diversity in the color and structure of the skin lesions is a challenging reason for automatic skin lesion segmentation. In this study, we used convolutional neural networks (CNN) as an efficient method for dermoscopic image segmentation. The main goal of this research is to recommend a novel architecture of deep neural networks for the injured lesion in dermoscopic images which has been improved by the convolutional layers based on the separable layers. By convolutional layers and the specific operations on the kernel of them, the velocity of the algorithm increases and the training parameters decrease. Additionally, we used a suitable preprocessing method to enter the images into the neural network. Suitable structure of the convolutional layers, separable convolutional layers and transposed convolution in the down sampling and up sampling parts, have made the structure of the mentioned neural network. This algorithm is named Sep-unet and could segment the images with 98% dice coefficient.
An Innovative Technique for Medical Image Segmentation Using Convolutional Neural Networks Optimized Through Stochastic Gradient Descent Taheri, Mohammad; Sadeghi, Faezeh; Koochari, Abbas
International Journal of Artificial Intelligence Vol 11 No 1: June 2024
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijai-01101.688

Abstract

Medical image segmentation is crucial due to its essential role in disease therapy. Various challenges such as hair artifacts, illumination variations, and different imaging acquisitions complicate this task. In this paper, we introduce a novel convolutional neural network (CNN) architecture designed to address these challenges. We also compare our method with two well-known architectures, Unet and FCN, to demonstrate the superiority of our approach. Our results, evaluated using four metrics, accuracy, dice coefficient, Jaccard index, and sensitivity show that our method outperforms the other two. We employed Jaccard distance and binary cross-entropy as the loss functions and used SGD+Nesterov as the optimization algorithm, which proved more effective than the Adam optimizer. In the preprocessing step, we included image resizing to speed up the process and image augmentation to enhance the evaluation metrics. As a postprocessing step, we applied a threshold technique to the algorithm's outputs to increase the contrast of the final images. This method was tested on two well-known and publicly available medical image datasets: PH2 for melanoma detection and Chest X-ray images for detecting chest lesions or COVID-19.
Co-Authors Koochari, Abbas Rastgarpour, Maryam Sharifi, Arash Taheri, Mohammad