No abstract

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

In recent days, the fast growth of populations leading to an increase in medically complicated cases, especially fast spread viral cases around the world. These phenomena increased demand on auto-diagnose systems to speed up the diagnosis process and reduce human contacts, especially for the COVID-19 pandemic using deep learning (DL). DL methods can successfully carry out these complicated works. A Deep Convolutional Neural Network (Deep CNN) is the most appropriate model for the medical image diagnosis process among DL techniques. This study focuses on follow-up and diagnosis of COVID-19 pneumonia cases. Deep CNN model can learn the chest computed tomography (CT) features properly synchronizing with the training options that involve the optimizer, number of epochs, and learning rate to get optimal accuracy with the lowest error rate. The auto diagnosis process aims to follow-up and diagnosis COVID-19 pneumonia and illuminate it from Streptococcus pneumonia and normal chest. Executed the present study on were 840 CT images of 24 patients from the Radiopedia database. Computed tomography (CT) is the best modality to visualize lung diseases, which own enough positions to interpret everything inside lung anatomy. Deep CNN model owns of enough layers and enable the model to extract and learn pneumonia features from the training set images. This process applied on MATLAB software. The model's result exhibits that the proposed deep CNN approach had an accuracy level of 99.37%.