International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 09 | Sep 2024
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p-ISSN: 2395-0072
Automated Breast Cancer Detection and Classification Using Convolutional Neural Networks: A Systematic Approach Swamy Eshwar Rohanth Baipilla1, Sasi Vardhan Talluri2, Naval Didharia3, Bharath Chandra Thota4 1,2,3,4 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, India
---------------------------------------------------------------------***--------------------------------------------------------------------Well, scientists train the CNNs by giving them a bunch of Abstract - Breast cancer is still a important cause of death for females among the world. It’s really important to catch it early and accurately classify it in order to improve patient outcomes. This paper introduces a cool way to automatically detect and classify breast cancer using Convolutional Neural Networks (CNNs). The method they propose uses the latest CNN architectures to extract features and classify the cancer, which leads to really accurate and reliable results. They trained and tested their models using a big dataset of mammographic images that were annotated. To see how well the models worked, they used performance metrics like accuracy, sensitivity, and AUC- ROC. The proved results stated that the CNN-based system they developed performed way better than traditional methods, so it could be a super useful tool for doctors and clinicians. If they integrate this automated system into clinical practice, it could make a big difference by catching cancer early, reducing misdiagnosis, and ultimately saving more lives.
labeled mammographic images. The networks learn to spot patterns and features that are associated with cancerous and non-cancerous growths. Once trained, the CNNs can then analyze new mammograms and provide accurate diagnostic results without the need for a radiologist. This not only reduces the workload for doctors, but also helps minimize diagnostic errors and improves the chances of catching cancer early[3].
Key Words: Breast cancer, Convolutional Neural Networks (CNNs), early detection, mammographic images, accuracy, sen-sitivity, AUC-ROC, automated system.
In this paper, we’re presenting a systematic approach to automating breast cancer detection and classification using CNNs. We’re using top-notch CNN architectures like VGG and ResNet, which have already proved themselves in other image recognition challenges. We tested our methodology using a big dataset of annotated mammographic images and used metrics like accu- racy, and AUC-ROC to evaluate our models. Our results show that the CNN-based system outperforms traditional methods by a mile, giving us a reliable tool for clinical diagnostics [8-10].
Lots of studies have looked into how effective CNNs are in detecting breast cancer. For example, Shen and their team in 2019 showed that deep learning models can boost the detection breast cancer on mammograms, performing just as well as experienced radiologists. Similarly, Ribli and their crew in 2018 used deep learning techniques to detect and classify lesions in mammograms, and they found huge improvements compared to traditional methods [4].
1.INTRODUCTION Breast cancer is most dangerous cause among all the women in the world and a main reason for cancer-related deaths. It’s super important to catch it early and get an accurate diagnosis to improve survival rates and patient outcomes. In the past, doctors relied on mammographic screening, which involves looking at mammograms to identify any signs of cancer. But let’s be real, this method has its flaws. Humans can make mistakes and there can be a lot of variation, which means some cases can be missed or falsely identified [1].
With the power of AI and CNNs, we’re making huge strides in detecting breast cancer early and accurately. It’s an exciting time in medical imaging, and we’re hopeful that these advancements will save lives and improve patient outcome [5- 7].
2.RELATED WORK
But Thanks to recent advances in Machine Learning and Artificial Intelligence, and mainly related to deep learning, we’re seeing some exciting progress in analyzing medical images. One cool thing in particular is Convolutional Neural Networks (CNNs), which are fancy algorithms that excel at recognizing images, including medical ones. They learn and picking process of important features from these images are automatic, making them perfect for tasks like detecting and classifying breast cancer [2].
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Deep learning techniques, specifically Convolutional Neural Networks (CNNs), had been gaining a huge attention in recent years for their application in medical analysis of images. The potential of CNNs are being observed my many studies in automated detection and classification of breast cancer, show- ing significant improvements over traditional methods [12].
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