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Sudeep Tanwar

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

E-mail: sudeep.tanwar@nirmauni.ac.in

Website: https/orcid.org/0000-0002-1776-4651

Research Interests: Sensor, , Wireless Networks, Computer Networks

Biography

Engineering Department. He has more than 19 years of teaching experience. Dr Tanwar received his BTech degree in Computer Engineering from Kurukshetra University, Kurukshetra, MTech (Hons) degree in Information Technology from the Guru Gobind Singh Indraprastha University, Delhi, and PhD degree in Computer Science and Engineering from Mewar University, India. Dr Tanwar's research interests include Blockchain Technology, Wireless Sensor Networks, Fog Computing, Smart Grid, and IoT. He has authored 02 books and edited 13 books, more than 200 technical papers, including top journals and top conferences, such as IEEE TNSE, TVT, TII, WCM, Networks, ICC, GLOBECOM, and INFOCOM. He has guided many students leading to ME/MTech and guiding students leading to PhD. He has been awarded as Outstanding Reviewer Award from FGCS, Elsevier, CEE, Elsevier, JNCA, Elsevier, DCN, Elsevier. (July 2018). Dr Tanwar initiated the research field of blockchain technology adoption in various verticals in 2017. His h-index is 38. Dr Tanwar actively serves his research communities in various roles. He is currently serving the editorial boards of Physical Communication, Computer Communications, International Journal of Communication System, and Security and Privacy. He has been awarded the best research paper awards from IEEE GLOBECOM 2018, IEEE ICC 2019, and Springer ICRIC-2019. He has served many international conferences as a member of the organizing committee, such as publication chair for FTNCT-2020, ICCIC 2020, WiMob2019, member of the advisory board for ICACCT-2021, ICACI 2020, workshop co-chair for CIS 2021, and general chair for IC4S 2019, 2020, ICCSDF 2020. Dr Tanwar is a final voting member for IEEE ComSoc Tactile Internet Committee in 2020. He is a Senior Member of IEEE, CSI, IAENG, ISTE, CSTA, and the member of the Technical Committee on Tactile Internet of IEEE Communication Society. 

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

By Shah Hemali Agrawal Smita Parita Oza Sudeep Tanwar Ahmed Alkhayyat

DOI: https://doi.org/10.5815/ijigsp.2023.04.04, 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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