Introduction: The determination of tumour extent is a major challenging task in brain tumour planning and quantitative evaluation. Magnetic Resonance Imaging (MRI) is one of the non-invasive technique has emanated as a front-line diagnostic tool for brain tumour without ionizing radiation. Objective: Among brain tumours, gliomas are the most common aggressive, leading to a very short life expectancy in their highest grade. In the clinical practice manual segmentation is a time consuming task and their performance is highly depended on the operator's experience. Methods: This paper proposes fully automatic segmentation of brain tumour using convolutional neural network. Further, it uses high grade gilomas brain image from BRATS 2015 database. The suggested work accomplishes brain tumour segmentation using tensor flow, in which the anaconda frameworks are used to implement high level mathematical functions. The survival rates of patients are improved by early diagnosis of brain tumour. Results: Hence, the research work segments brain tumour into four classes like edema, non-enhancing tumour, enhancing tumour and necrotic tumour. Brain tumour segmentation needs to separate healthy tissues from tumour regions such as advancing tumour, necrotic core and surrounding edema. This is an essential step in diagnosis and treatment planning, both of which need to take place quickly in case of a malignancy in order to maximize the likelihood of successful treatment.

Introduction:Vaginal discharge is one of the common reasons for gynecological consultation. Many of the causes of vaginitis have a disturbed vaginal microbial ecosystem associated with them. Effective treatment of vaginal discharge requires that the etiologic diagnosis be established and identifying the same offers a precious input to syndromic management and provides an additional strategy for human immunodeficiency virus prevention. The present study was thus carried out to determine the various causes of vaginal discharge in a tertiary care setting.Materials and Methods:A total of 400 women presenting with vaginal discharge of age between 20 and 50 years, irrespective of marital status were included in this study and women who had used antibiotics or vaginal medication in the previous 14 days and pregnant women were excluded.Results:Of the 400 women with vaginal discharge studied, a diagnosis was established in 303 women. Infectious causes of vaginal discharge were observed in 207 (51.75%) women. Among them, bacterial vaginosis was the most common cause seen in 105 (26.25%) women. The other infections observed were candidiasis alone (61, 15.25%), trichomoniasis alone (12, 3%), mixed infections (22, 5.5%) and mucopurulent cervicitis (7 of the 130 cases looked for, 8.46%). Among the non-infectious causes, 72 (18%) women had physiological vaginal discharge and 13 (3.3%) women had cervical in situ cancers/carcinoma cervix.Conclusion:The pattern of infectious causes of vaginal discharge observed in our study was comparable with the other studies in India. Our study emphasizes the need for including Papanicolaou smear in the algorithm for evaluation of vaginal discharge, as it helps establish the etiology of vaginal discharge reliably and provides a valuable opportunity to screen for cervical malignancies.

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Objective: Generally, lung cancer is the abnormal growth of cells that originates in one or both lungs. Finding the pulmonary nodule helps in the diagnosis of lung cancer in early stage and also increase the lifetime of the individual. Accurate segmentation of normal and abnormal portion in segmentation is challenging task in computer-aided diagnostics. Methods: The article proposes an innovative method to spot the cancer portion using Otsu's segmentation algorithm. It is followed by a Support Vector Machine (SVM) classifier to classify the abnormal portion of the lung image. Results: The suggested methods use the Otsu's thresholding and active contour based segmentation techniques to locate the affected lung nodule of CT images. The segmentation is followed by an SVM classifier in order to categorize the affected portion is normal or abnormal. The proposed method is suitable to provide good and accurate segmentation and classification results for complex images. Conclusion: The comparative analysis between the two segmentation methods along with SVM classifier was performed. A classification process based on active contour and SVM techniques provides better than Otsu's segmentation for complex lung images.

Generally the segmentation refers, the partitioning of an image into smaller regions to identify or locate the region of abnormality. Even though image segmentation is the challenging task in medical applications, due to contrary image, local observations of an image, noise image, non uniform texture of the images and so on. Many techniques are available for image segmentation, but still it requires to introduce an efficient, fast medical image segmentation methods. This research article introduces an efficient image segmentation method based on K means clustering integrated with a spatial Fuzzy C means clustering algorithms. The suggested technique combines the advantages of the two methods. K means segmentation requires minimum computation time, but spatial Fuzzy C means provides high accuracy for image segmentation. The performance of the proposed method is evaluated in terms of accuracy, PSNR and processing time. It also provides good implementation results for MRI brain image segmentation with high accuracy and minimal execution time. After completing the segmentation the of abnormal part of the input MRI brain image, it is compulsory to classify the image is normal or abnormal. There are many classifiers like a self organizing map, Back propagation algorithm, support vector machine etc., The algorithm helps to classify the abnormalities like benign or malignant brain tumour in case of MRI brain image. The abnormality is detected based on the extracted features from an input image. Discrete wavelet transform helps to find the hidden information from the MRI brain image. The extracted features are trained by Back Propagation Algorithm to classify the abnormalities of MRI brain image.