A Combined PCA-MLP Network for Early Breast Cancer Detection
Breast cancer is the second most responsible for all cancer types and has been the cause of numerous deaths over the years, especially among women. Any improvisation of the existing diagnosis system for the detection of cancer can contribute to minimizing the death ratio. Moreover, cancer detection at an early stage has recently been a prime research area in the scientific community to enhance the survival rate. Proper choice of machine learning tools can ensure early-stage prognosis with high accuracy. In this paper, we have studied different machine learning algorithms to detect whether a patient is likely to face breast cancer or not. Due to the implicit behavior of early-stage features, we have implemented a multilayer perception model with the integration of PCA and suggested it to be more viable than other detection algorithms. Our 4 layers MLP-PCA network has obtained the best accuracy of 100% with a mean of 90.48% accuracy on the BCCD dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningBreast Cancer DetectionPrognosisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
As early detection of breast cancer strongly favors successful therapeutic outcomes, there is major commercial interest in optimizing breast cancer screening. However, current risk prediction models achieve modest perfor…
Ensemble of radiomics and ConvNeXt for breast cancer diagnosis
Early diagnosis of breast cancer is crucial for improving survival rates. Radiomics and deep learning (DL) have shown significant potential in assisting radiologists with early cancer detection. This paper aims to critic…
Breast Cancer DetectionDeep Learning Approach for Early Stage Lung Cancer Detection
Lung cancer is the leading cause of death among different types of cancers. Every year, the lives lost due to lung cancer exceed those lost to pancreatic, breast, and prostate cancer combined. The survival rate for lung …
Computed Tomography (CT)Deep LearningA lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification
Accurate and early detection of breast cancer is essential for successful treatment. This paper introduces a novel deep-learning approach for improved breast cancer classification in histopathological images, a crucial s…
Cancer ClassificationComputational EfficiencyBreast Cancer Segmentation using Attention-based Convolutional Network and Explainable AI
Breast cancer (BC) remains a significant health threat, with no long-term cure currently available. Early detection is crucial, yet mammography interpretation is hindered by high false positives and negatives. With BC in…
Segmentation