Agrawal Smita

Work place: Institute of Technology, Nirma University, Ahmedabad, Gujarat, 382481, Ahmedabad, Gujarat, India



Research Interests: Parallel Computing,


Dr Smita Agrawal is working as an Assistant Professor in Computer Science and Engineering Department since 2009. She has a teaching experience of over 14 years. She received her Master of Computer Applications degree from Gujarat Vidhyapith in 2004. She completed her doctoral studies in the field of Big Data Analytics from CHARUSAT University in 2019. She works in the area of Big Data Analytics, Parallel Processing, Web Development and IoT. She is a Senior Member of IEEE, Member of CSI, ACM, and ISTE. She has published several Scopus/SCIE-indexed research papers in national and international conferences and journals. Dr Smita has conducted ISTE approved short-term training Programme in the field of Web Services using PHP. She is involved in teaching courses at both undergraduate and postgraduate levels. She also serves as a member of the Programme committees and session chair for international conferences and as a reviewer for indexed international journals.

Author Articles
Mammogram Pre-processing Using filtering methods for Breast Cancer Diagnosis

By Shah Hemali Agrawal Smita Parita Oza Sudeep Tanwar Ahmed Alkhayyat

DOI:, Pub. Date: 8 Aug. 2023

Cancer is the second most found disease, and Breast cancer is the most common in women. Breast cancer is curable and can reduce mortality, but it needs to be identified early and treated accordingly. Radiologists use different modalities for the identification of Breast cancer. The superiority of Mammograms over other modalities is like minor radiation exposure and can identify different types of cancers. Therefore, mammograms are the most frequently used imaging modality for Breast Cancer Diagnosis. However, noise can be added while capturing the image, affecting the accuracy and analysis of the result. Therefore, using different filtering techniques to pre-process mammograms can enhance images and improve outcomes. For the study, the MIAS dataset has been used. This paper gives a comparative study on filters for Denoising and enhancement of mammograms. The study focuses on filters like Box Filter, Averaging filter, Gaussian Filter, Identical Filter, Convolutional 2D Filter, Median Filter, and Bilateral Filter. Performance measures used to compare these filters are Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and Peak Signal-to-noise Ratio (PSNR). All Performance measures are evaluated for all images of MIAS dataset and compared accordingly. Results show that Gaussian Filter, Median Filter, and Bilateral Filter give better results than other filters.

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