Analysis and Design of Deep Learning Algorithms for Retinal Image Classification for Early Detection of Diabetic Retinopathy

This paper presents a far reaching survey of the standard and use of deep learning in retinal image investigation. Many eye ailments regularly lead to visual impairment without legitimate clinical determination and clinical treatment. For instance, diabetic retinopathy (DR) is one such illness in which the retinal veins of natural eyes are harmed. The ophthalmologists analyze DR dependent on their expert information that is work escalated. With the advances in image preparing and man-made reasoning, Personal Computer vision-based methods have been applied quickly and broadly in the field of clinical images investigation. The important deep learning algorithms such as CNN Convolution Neural Network, ConvNet based algorithm, LCD net and Deep CNN, their working and main features of some of these standard Deep Learning algorithm are analyzed in detailed. Proposed algorithm will become more reliable accurate by introducing new features as well as better quality input by using advance algorithm of image processing.

Spontaneous Regression of a Clival Chordoma: An Unusual Case Report

Introduction Chordoma is a malignant and aggressive tumor originating from remnants of the primitive notochord and usually involving the axial skeleton. Spontaneous regression of clival chordomas was described recently. We present the third case report of spontaneous regression of a clival chordoma and discuss similarities of cases and implications for clinical practice. Case Description We present the case of a previously healthy 21-year-old Caucasian woman who presented with progressive holocranial headache for 3 months, which encouraged image investigation. Magnetic resonance imaging (MRI) revealed an osteolytic clival lesion hyperintense in T2 and hypointense in T1 images. After 2 months of initial evaluation and surgical proposal, she repeated MRI to allow use for intraoperative neuronavigation. Surprisingly, there was tumor regression. Discussion The present reported case is somehow different from previous ones and does not share an underlying inflammatory/immunological recognizable fact, being interpreted by us as a spontaneous partial regression of the tumor. We highlight the need for continuous investigation of chordoma regression to uncover the underlying mechanisms.

A 3D CNN Classification Model for Accurate Diagnosis of Coronavirus Disease 2019 using Computed Tomography Images

The coronavirus disease (COVID-19) has been spreading rapidly around the world. As of August 25, 2020, 23.719 million people have been infected in many countries. The cumulative death toll exceeds 812,000. Early detection of COVID-19 is essential to provide patients with appropriate medical care and protect uninfected people. Leveraging a large computed tomography (CT) database from 1,112 patients provided by China Consortium of Chest CT Image Investigation (CC-CCII), we investigated multiple solutions in detecting COVID-19 and distinguished it from other common pneumonia (CP) and normal controls. We also compared the performance of different models for complete and segmented CT slices. In particular, we studied the effects of CT-superimposition depths into volumes on the performance of our models. The results show that the optimal model can identify the COVID-19 slices with 99.76% accuracy (99.96% recall, 99.35% precision and 99.65% F1-score). The overall performance for three-way classification obtained 99.24% accuracy and the area under the receiver operating characteristic curve (AUROC) of 0.9986. To the best of our knowledge, our method achieves the highest accuracy and recall with the largest public available COVID-19 CT dataset. Our model can help radiologists and physicians perform rapid diagnosis, especially when the healthcare system is overloaded.

Efficient Pneumonia Detection for Chest Radiography Using ResNet-Based SVM

Chest radiography has a significant clinical utility in the medical imaging diagnosis, as it is one of the most basic examination tools. Pneumonia is a common infection that rapidly affects human lung areas. So, finding an advanced automated method to detect Pneumonia is assigned to be one of the most recent issues, which is still prohibitively expensive to mass adoption, especially in the developing countries. This article presents an innovative approach for distinguishing the residence of pneumonia by embedding computational techniques to chest x-rays images which eliminating the demands for single-image investigation and significantly decrease the total costs. Recent advances in deep learning achieved remarkable results in image classification on different domains; however, its application for Pneumonia diagnosis is still restricted. Hence, the main focus is to provide an investigation that will improve the research in this area, presenting a new proposal to the applications of pre-trained convolutional neural networks (CNNs) as a stage of features extraction to detect this disease. Specifically, we propose to combine deep residual neural networks (ResNets), which extract the hierarchical features from the individual x-ray images with the boosting algorithm to select the salient features, and support vector machine for classification (AdaBoost-SVM). After conducting the performance analysis on the available dataset, we have concluded that the precision of the introduced scheme in Pneumonia classification is superior to the most concurrent approaches, resulting in a great improvement in clinical outcomes.

Melanoma Skin Cancer Detection

Skin cancer growth is viewed as one of the most Hazardous type of the Cancers found in Humans. Nowadays skin cancer is found in different kinds for example Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the generally flighty. The detection of Melanoma disease in beginning period can be helpful for cure it. Computer vision can play big role in Portrayal Analysis also it has been examined by many existing frameworks. In this paper, we present a Computer helped strategy for the recognition of Melanoma Skin Cancer utilizing Image Processing instruments. The contribution to the framework is the skin lesion picture and after that by applying novel picture preparing strategies, it investigates it to finish up about the nearness of skin malignancy. The Lesion Image investigation instruments checks for the different Melanoma parameters Like Asymmetry, Border, Color, Diameter,(ABCD) and so on by surface, size and shape examination for picture division and highlight stages. The extricated highlight parameters are utilized to characterize the picture as Normal skin and Melanoma cancer growth injury.

Spontaneous Bilateral Sternoclavicular Joint Septic Arthritis and Lumbar Discitis: An Unusual Case in a Healthy Adult

Introduction. Septic arthritis of the sternoclavicular (SC) joint is a rare condition. Typically, it presents in patients with risk of infection and is usually unilateral. In this report, we describe a case of spontaneous bilateral sternoclavicular joint infection of an otherwise healthy adult. Case Presentation. A 67-year-old man presented in our hospital complaining of 2-week history of neck and chest pain which was radiating to his shoulders bilaterally. Clinical examination revealed erythema and swelling of the sternoclavicular area. Inflammatory markers were raised. Image investigation with CT and MRI was undertaken and verified the presence of bilateral sternoclavicular joint infection. The patient received prolonged course of intravenous antibiotics since his admission. The patient was discharged in a good condition and followed up in clinic. Conclusion. High index of clinical suspicion of SC joint infection is important for early diagnosis to avoid further complications.