TY  - EJOU
AU  - Priyadarshni, Vaishnawi 
AU  - Sharma, Sanjay Kumar 
AU  - Rahmani, Mohammad Khalid Imam 
AU  - Kaushik, Baijnath 
AU  - Almajalid, Rania 

TI  - Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 78
IS  - 2
SN  - 1546-2226

AB  - Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of various techniques, where the fusion of deep learning (DL), traditional machine learning (ML) techniques, and enhanced classification models have been deployed using the curated dataset. The analysis outcome proves that the proposed enhanced RF (ERF), enhanced DT (EDT) and enhanced LR (ELR) models for BC detection outperformed most of the existing models with impressive results.
KW  - Autoencoder; breast cancer; deep neural network; convolutional neural network; image processing; machine learning; deep learning

DO  - 10.32604/cmc.2024.044963