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Breast cancer is a leading cause of death among women, and its incidence is rising. Mammography has been shown to be effective in screening asymptomatic women to detect occult breast cancer and to reduce mortality by as much as 30% in women aged between 50 and 69 years. Our objective is to develop a CAD system to automatically detect, analyze, and to classify the different features in mammographic images through image processing technique. The feasibility of the proposed approach was explored on the images, and the sensitivity rate is 89% and the specificity is 93%.

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Cervical Cancer is the abnormal growth of tissues in the lower, narrow part of the uterus (womb) called the Cervix which connects the main body of the uterus, to the vagina or birth canal. Cervical cancer is one of the most common types of cancer that can be seen in women worldwide. Early detection and proper diagnosis can prevent the severity level and reduce the death rates .In this paper, we have proposed an automated diagnosis system of cervical cancer using texture features and Multiclass SVM (Support Vector Machine) Classifier in MRI images. Initially the MRI images are pre-processed to remove undesirable noises and other effects. After pre-processing, the image is segmented by Region growing method to obtain the region of interest. Texture features are extracted from the segmented region. Almost 22 features are extracted at the region of a segmented area and then passed on to Multiclass SVM Classifier to detect if the given image is cancerous or not. The results of the proposed techniques provide effective results for classifying cancerous and the non-cancerous image.

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Human brain contain neurons which generate electrical signals, this can be recorded through electro encephalograph(EEG). Sensory motor cortices are  responsible for motor activity i.e., various body movements, among which wrist movement reveals frequency change in Alpha & Beta bands of EEG signal. The aim of this approach is to calculate frequency changes responsible for various wrist movements such as flexion, extension, clockwise rotation and anticlockwise rotation, pronation and supination of female in both eyes open and eyes close conditions using FFT, wavelet transform classifier, where the largest set of EEG data is reduced to dimensions and the spectral frequencies for particular wrist movements are classified and the statistical analysis is done of various trials for both eyes open and eyes close conditions in both time domain and frequency domain and the mean and standard deviation of various trials will be compared for eyes open and eyes close condition in both time domain and frequency domain and these values can be implemented for neuro prosthetic applications.

Studies have shown a lower risk for verbal memory decline following dominant anterior temporal lobectomy (ATL) among patients with poor, presurgical verbal memory scores. It is unclear however, if the risk of decline is increased in patients who also have reduced visual memory. Objective and subjective memory outcome following left ATL was examined in twelve patients with reduced presurgical visual and verbal memory scores. Only one patient demonstrated a meaningful decline in memory scores, with a decline in visual memory following surgery. Presurgically, this patient demonstrated poor memory bilaterally on Wada testing and small discrepancy in hippocampal volumes. She was also one of two patients who continued to have seizures post‐surgery. This preliminary study suggests that patients with unilateral, left TLE and poor verbal and visual memory are unlikely to show meaningful memory declines following left ATL, particularly if they demonstrate expected patterns on Wada testing, hippocampal volume discrepancy (left < right), and postsurgical seizure‐freedom.