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Breast cancer is second most commonly diagnosed cancer worldwide. In order to find the cure, it is necessary to quickly diagnose the disease accurately and treat it based on the kind of symptoms appeared. Breast cancer has several classifications, which may help to determine the best treatment. The most important of these classifications are binary classification, either benign or malignant. If the cancer is in benign stage, less invasive and risk of treatments is used than for malignant stage. The main cause of breast cancer is when a single cell or group of cells escapes from the usual controls, that regulate cellular growth and begins to multiply and spread. This activity may result in a mass, tumor or neoplasm. The present paper implies the edge detection techniques for the cancer cell detection purpose. The present paper deals with observation of breast cancer classification through Image Processing using the various filters which are mainly gradient based Roberts and Sobel. Laplacian based edge detector which is Canny edge detector. The various aspects and the implementation of above mentioned filters has been put across in the present paper. The images and data sample have been taken from the Digital Database for Screening Mammography (DDSM) and American cancer society and an effort has been made for the detection of malignant cells responsible for cancer

Energy conservation is the primary concern in Wireless Sensor Networks (WSNs). In most applications nodes are randomly deployed in the sensing field. Therefore, it is impracticable to replace their energy sources. Hence, energy efficient routing is necessary in order to prolong network life time several times. In the proposed work we utilize an Energy Efficient Data Forwarding scheme (EEMDF) to route the data effectively in multilayered architecture in large scale wireless sensor networks. We employ the energy metrics to discover the optimal path for routing the aggregated data. Specifically, we will analyze the network life time and energy consumption for the proposed work. Performance evaluation is done by simulation which shows the effectiveness of the work carried out so far.

Noise removal from a non-stationary signal like Electrocardiogram (ECG) signal is a major problem. Moreover, nullifying different noises using different adaptive algorithms –Least Mean Square (LMS), Normalized Least Mean Square (NLMS) etc. from ECG signal is one of the advance studies in biomedical signal processing. In this paper, we will discuss how we can remove different type of noises like 50Hz Power-Line Interference, Base-line Wandering and Muscle Contraction noise from an ECG signal using an adaptive filter. Different performance parameters such as, Signal-to-Noise Ratio, Mean Square Error and Root Mean Square Error are also calculated to compare the results. Real time data has been collected from MITBIH arrhythmia database. At the end, results show the better performance of adaptive NLMS filter for removing different noises over adaptive LMS filter.