M.suriya priyadharsini
@research scholar
computer science
Bishop heber college, Bharathidasan university ,
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Science, Computer Vision and Pattern Recognition, Signal Processing, Computer Science Applications
Scopus Publications
Scholar Citations
Scholar h-index
Scopus Publications
M. Suriya Priyadharsini and J. G. R. Sathiaseelan
Springer Science and Business Media LLC
M. Suriya Priyadharsini and J.G.R. Sathiaseelan
Inderscience Publishers
Suriya Priyadharsini .M and J.G.R. Sathiaseelan
Auricle Technologies, Pvt., Ltd.
Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. There are numerous procedures and approaches for detecting cancer in the tissues of the breast. This work presents the image processing, segmentation, and deep learning methodologies and approaches for the diagnosis of breast cancer. This research will help people make better decisions and use trustworthy techniques to find breast cancer early enough to save a woman's life. Pre-processing, segmentation, and classification are some of this system's steps. We've included a thorough study of several techniques or processes, along with information on how they're used and how performance is measured. The stated results lead to the conclusion that, in order to increase the chances of surviving breast cancer, it is crucial to develop new procedures or techniques for early diagnosis. For researchers to effectively diagnose breast cancer, segmentation and classification phases are also difficult. Therefore, the precise diagnosis and categorization of breast cancer still require the use of more advanced equipment and techniques.
Suriya Priyadharsini M and J.G.R. Sathiaseelan
IEEE
Ultrasound imaging has been presented to deliver a non-invasive and non-destructive method either in manufacturing or medicinal field. Ultrasound is commonly used in medicine to diagnose prenatal and malignant sickness. This is connected to the creation of speckles in ultrasound images, which type them difficult to analyse quickly. A new switching-Mode linear filtering (NSMLF) approach is proposed aimed at restoring images that have been heavily contaminated by salt and pepper noise. The method is used to create a Method. Prior to estimate, the new technique incorporates the idea of replacing corrupted values with linear predictions. For this, a new simple linear predictor is being created. The purpose of the method and technique is to eliminate high-density salt and pepper noise (HD-SPN) from photographs. The proposed technique progresses image quality by having a higher peak to signal ratio (PSNR), a lower Mean Square Error (MSE), greater edge retention, and less streaking. With less computing complexity, good presentation is attained. In terms of graphic and quantifiable outcomes, the presentation is related to that of numerous existing systems. The proposed scheme and algorithm's performance is established.