Mammogram Breast Tumor Abnormalities Detection Using DeepCNN with Discrete Cosine Transform Features International Journal of Intelligent Systems and Applications in Engineering, 2023
Breast Cancer Analytics Classification using MEFBUD DCNN Techniques Suriya Priyadharsini .M, J.G.R. Sathiaseelan International Journal on Recent and Innovation Trends in Computing and Communication, 2023 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.
Advanced DEEPCNN breast cancer mammogram image detection and classification with butterfly optimisation algorithm M. Suriya Priyadharsini, J.G.R. Sathiaseelan International Journal of Computational Biology and Drug Design, 2023 A major aspect influencing human health is breast cancer. Mammography, fine needle aspiration, and surgical biopsy are some of the evolving diagnosis methods for this problem. Pathology images are used to diagnose breast cancer. Breast tumour surgery allows doctors to microscopically study breast tissue. Traditional methods use a cuckoo-optimised radial basis neural network. Earlier RBN algorithms handled feature extraction and reduction differently. To reduce unneeded complexity, outperform convolutional neural network (CNN) for feature extraction and classification. The Butterfly optimisation technique suggests a CNN. Zernike moments' scale, interpretation, and rotation similarity lets us bypass numerous pre-processing steps. The picture dataset was created from tumour treatment archives. The Butterfly optimisation method feeds the DCNN training data. DCNN removes, reduces, and classifies features. By determining the number of historical periods and training images for Deep CNN, optimisation improves efficiency and reduces error rates. This approach projects normal, benign, and malignant. The model achieves sensitivity, accuracy, specificity, F1 score, and recall by contrasting RBF with cuckoo search.
A New Switching-Mode Liner Filtering Scheme and Algorithm for Noise Removal in Medical Images Suriya Priyadharsini M, J.G.R. Sathiaseelan 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering Icacite 2022, 2022 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.
RECENT SCHOLAR PUBLICATIONS
The New Robust Adaptive MedianFilter for Denoising Cancer ImagesUsing Image Processing Techniques JGRS M Suriya Priyadharsini INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 16 (35), 2813-2821 , 2023 2023 Citations: 6
Mammogram Breast Tumor Abnormalities Detection Using DeepCNN with Discrete Cosine Transform Features. JGR Suriya Priyadharsini ,M., & Sathiaseelan International Journal of Intelligent Systems and Applications in Engineering … , 2023 2023 Citations: 2
Breast Cancer Analytics Classification using MEFBUD DCNN Techniques M SuriyaPriyadharsini, DJGR Sathiaseelan International Journal on Recent and Innovation Trends in Computing and … , 2023 2023
Advanced DEEPCNN breast cancer mammogram image detection and classification with butterfly optimisation algorithm MS Priyadharsini, JGR Sathiaseelan International Journal of Computational Biology and Drug Design 15 (5), 357-376 , 2023 2023
High Noise Density Median Filter Method for Denoising Cancer Images Using Image Processing Techniques M SuriyaPriyadharsini, DJGR Sathiaseelan INTERNATIONAL JOURNAL OF COMPUTER SCIENCE NETWORK SECURITY 22 (NO:11), 308 … , 2022 2022
A New Switching-Mode Liner Filtering Scheme and Algorithm for Noise Removal in Medical Images SJGR M Suriya Priyadharsini 2022 2nd International Conference on Advance Computing and Innovative … , 2022 2022 Citations: 1
PIXEL HIGH DENSITY NOISE FILTER METHOD FOR DENOISINGIMAGES USING IMAGE PROCESSING TECHNIQUES M SuriyaPriyadharsini, DJGR Sathiaseelan2 2022
Vigor Effectual Video Compression Mechanism for Wireless Sensor Network Using Edge Feature Reduction Algorithm M Suriyapriyadharsini, JGR sathiaseelan 2022
CONVOLUTION NEURAL NETWORK AND MACHINE LEARNING FOR THE PREDICTION OF BREAST CANCER: A COMPARATIVE STUDY M Suriyapriyadharsini, JGR sathiaseelan Nehru E- Journal | ISSN: 2349-9052, 34-42 , 2021 2021
Performance Analysis of SVM in Breast Cancer Classification: A Survey M Suriyapriyadharsini, JGR sathiaseelan 2021 , 2021 2021
MOST CITED SCHOLAR PUBLICATIONS
The New Robust Adaptive MedianFilter for Denoising Cancer ImagesUsing Image Processing Techniques JGRS M Suriya Priyadharsini INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY 16 (35), 2813-2821 , 2023 2023 Citations: 6
Mammogram Breast Tumor Abnormalities Detection Using DeepCNN with Discrete Cosine Transform Features. JGR Suriya Priyadharsini ,M., & Sathiaseelan International Journal of Intelligent Systems and Applications in Engineering … , 2023 2023 Citations: 2
A New Switching-Mode Liner Filtering Scheme and Algorithm for Noise Removal in Medical Images SJGR M Suriya Priyadharsini 2022 2nd International Conference on Advance Computing and Innovative … , 2022 2022 Citations: 1
Breast Cancer Analytics Classification using MEFBUD DCNN Techniques M SuriyaPriyadharsini, DJGR Sathiaseelan International Journal on Recent and Innovation Trends in Computing and … , 2023 2023
Advanced DEEPCNN breast cancer mammogram image detection and classification with butterfly optimisation algorithm MS Priyadharsini, JGR Sathiaseelan International Journal of Computational Biology and Drug Design 15 (5), 357-376 , 2023 2023
High Noise Density Median Filter Method for Denoising Cancer Images Using Image Processing Techniques M SuriyaPriyadharsini, DJGR Sathiaseelan INTERNATIONAL JOURNAL OF COMPUTER SCIENCE NETWORK SECURITY 22 (NO:11), 308 … , 2022 2022
PIXEL HIGH DENSITY NOISE FILTER METHOD FOR DENOISINGIMAGES USING IMAGE PROCESSING TECHNIQUES M SuriyaPriyadharsini, DJGR Sathiaseelan2 2022
Vigor Effectual Video Compression Mechanism for Wireless Sensor Network Using Edge Feature Reduction Algorithm M Suriyapriyadharsini, JGR sathiaseelan 2022
CONVOLUTION NEURAL NETWORK AND MACHINE LEARNING FOR THE PREDICTION OF BREAST CANCER: A COMPARATIVE STUDY M Suriyapriyadharsini, JGR sathiaseelan Nehru E- Journal | ISSN: 2349-9052, 34-42 , 2021 2021
Performance Analysis of SVM in Breast Cancer Classification: A Survey M Suriyapriyadharsini, JGR sathiaseelan 2021 , 2021 2021
