Addressing Data Intrinsic Characteristics for Augmentation for Breast Cancer Classification
Author: 
Armaan GargAuthors Info & Claims
Armaan GargAuthors Info & ClaimsPages 299 - 300
Abstract
Breast cancer is the most frequently diagnosed cancer among females worldwide. The task of correctly diagnosing cancer using histopathology in its very earlier stages is a challenging and critical task. Most of the present machine learning techniques require a lot of data to analyze and predict a benign tumour in its early stages, and such data is not available readily. In this paper, we propose the idea of data augmentation of breast cancer tissue images by addressing data intrinsic characteristics. The aim is to detect the micro presence of the tumour cells and highlight it over multiple synthetic images for classifiers to predict benign tumours in very early stages with high accuracy. The initial experimental analysis highlights the proposed technique’s impact and significance in boosting the performance of standard classifier(s).
References
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Armaan Garg, Vishali Aggarwal, and Neeti Taneja. 2020. Classification of imbalanced data: Addressing data intrinsic characteristics. Futuristic Trends in Networks and Computing Technologies (2020), 264–277.
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Evelyn Lauder. 2021. Breast cancer statistics and resources: Breast Cancer Research Foundation: BCRF. https://www.bcrf.org/breast-cancer-statistics-and-resources/
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Parita Oza, Paawan Sharma, Samir Patel, Festus Adedoyin, and Alessandro Bruno. 2022. Image augmentation techniques for Mammogram analysis. Journal of Imaging 8, 5 (2022), 141.
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Alexander Rakhlin, Alexey Shvets, Vladimir Iglovikov, and Alexandr A. Kalinin. 2018. Deep convolutional neural networks for breast cancer histology image analysis. Lecture Notes in Computer Science(2018), 737–744.
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Information & Contributors
Information
Published In
January 2023
357 pages
ISBN:9781450397971
DOI:10.1145/3570991
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 January 2023
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- Extended-abstract
- Research
- Refereed limited
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CODS-COMAD 2023
CODS-COMAD 2023: 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)
January 4 - 7, 2023
Mumbai, India
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Overall Acceptance Rate 197 of 680 submissions, 29%
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