COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

This paper explores the possibility of applying techniques for segmenting the regions of med ical image. For this we need to investigate the use of different techniques which helps for detection and classification of image regions. We also discuss some segmentation methods classified by researchers. Region classification is an essential process in the visualizat ion of brain t issues of MRI. Brain image is basically classified into three regions; WM, GM and CSF. The forth region can be called as the tumor region, if the image is not normal. In the paper; Segmentation and characterization of Brain M R image regions using SOM and neuro fuzzy techniques, we integrate Self Organizing Map(SOM) and Neu ro Fu zzy scheme to automatically extract WM, GM , CSF and tumor reg ion of brain MRI image tested on three normal and three abnormal brain M RI images. Now in this paper this scheme is further tested on axial v iew images to classify the regions of brain M RI and compare the results fro m the Keith"s database. Using some statistical tests like accuracy, precision, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, false negative rate, likelihood ratio positive, likelihood ratio negative and prevalence of disease we calculate the effectiveness of the scheme.

Nowadays the most common type of cancer in women is breast cancer. This is the second main cause of cancer deaths in women. Digital mammography is the technique which is used to examine the breast. This is very much useful for the detection of breast diseases in women. The automatic detection of tumor or some type of deformity in the medical imaging is done by many researchers to develop some algorithms and methods. In this paper we are using SOM and Fuzzy c-means

Data Mining is an aggregate of various techniques and methods to extract out information from a large database in simple, understandable and usable form. A variety of data mining techniques has been applied successfully in the health care system and is attracting the attention of scientist and researchers throughout the world. The present research study describes various cognitive psychological theories of emotions and applications of different data mining methods. The study also emphasizes the data mining methods significant in the cognitive psychological emotion analysis.

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