Breast Cancer Detection Using Autoencoder

Registration ID: IJNRD_223822

Published ID: IJNRD2406276

DOI: Click Here to Get

N. V.S. Suma Varsha , K. Jahnavi , N. Sai Lavanya , MD. Rayan

Convolutional Neural Networks, Convolutional Autoencoders, Dimensionality reduction.

Breast cancer stands as the predominant ailment among females, with a prevalence of 2.1 million cases annually, leading to over 70,000 fatalities globally. Convolutional Neural Networks (CNNs) have emerged as pivotal tools for image classification tasks, demonstrating proficiency in analyzing breast cancer images. While conventional feature extraction methods have been widely employed, the utilization of CNNs for both feature extraction and classification has garnered attention. In pursuit of heightened classification accuracy and efficiency, Convolutional Autoencoders (CAEs) are integrated to extract intricate features and reduce dimensionality, subsequently augmenting CNN-based classification. This project aims to enhance classification accuracy by integrating feature extraction mechanisms, namely CAEs with dimensionality reduction, with CNNs, across diverse breast cancer image modalities.

Published Paper   E-Certificate

N. V.S. Suma Varsha, K. Jahnavi, N. Sai Lavanya, & MD. Rayan (June-2024). Breast Cancer Detection Using Autoencoder. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(6), c829-c834. https://ijnrd.org/papers/IJNRD2406276.pdf

Volume 9 Issue 6, June-2024

Pages : c829-c834

Paper Reg. ID: IJNRD_223822

Published Paper Id: IJNRD2406276

Downloads: 000121979

Research Area: Computer Science & Technology 

Country: Vizianagaram, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2406276.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2406276

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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