Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases.

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Brain tumor is a mass of anomalous cells in the brain. Medical imagining techniques have a vital role in the diagnosis of brain tumors. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) techniques are the most popular techniques to localize the tumor area. Brain tumor segmentation is very important for the diagnosis of tumors. In this paper, we introduce a framework to perform brain tumor segmentation, and then localize the region of the tumor, accurately. The proposed framework begins with the fusion of MR and CT images by the Non‐Sub‐Sampled Shearlet Transform (NSST) with the aid of the Modified Central Force Optimization (MCFO) to get the optimum fusion result from the quality metrics perspective. After that, image interpolation is applied to obtain a High‐Resolution (HR) image from the Low‐Resolution (LR) ones. The objective of the interpolation process is to enrich the details of the fusion result prior to segmentation. Finally, the threshold and the watershed segmentation are applied sequentially to localize the tumor region, clearly. The proposed framework enhances the efficiency of segmentation to help the specialists diagnose brain tumors.

Corona Virus Disease-2019 (COVID-19) continues to spread rapidly in the world. It has dramatically affected daily lives, public health, and the world economy. This paper presents a segmentation and classification framework of COVID-19 images based on deep learning. Firstly, the classification process is employed to discriminate between COVID-19, non-COVID, and pneumonia by Convolutional Neural Network (CNN). Then, the segmentation process is applied for COVID-19 and pneumonia CT images. Finally, the resulting segmented images are used to identify the infected region, whether COVID-19 or pneumonia. The proposed CNN consists of four Convolutional (Conv) layers, four batch normalization layers, and four Rectified Linear Units (ReLUs). The sizes of Conv layer used filters are 8, 16, 32, and 64. Four maxpooling layers are employed with a stride of 2 and a 2 × 2 window. The classification layer comprises a Fully-Connected (FC) layer and a soft-max activation function used to take the classification decision. A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images. The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

This paper presents a computer-based framework for the segmentation of medical eye images. Also, the proposed framework achieves the detection of exudates in medical eye images for better diagnosis of maculopathy disease. The proposed framework begins with fuzzy image enhancement of eye images for contrast enhancement in order to enhance the objects representation of the images. After that, the segmentation process is performed to determine the optic disc and blood vessels to remove them. The next step is detecting the region of interest edges in exudates. A gradient process is also performed on the image and the histogram of gradient is evaluated. Accumulative histogram is further generated for discrimination between image with and without exudates. A threshold histogram curve is generated based on predefined images with and without exudates for classification of images in the testing phase. The simulation results prove that the proposed framework has an appreciated performance.

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