International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 02 | Feb 2024
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p-ISSN: 2395-0072
Breast Cancer Detection using Computer Vision Janhavi Tingre
School of Computer Engineering & Technology MIT World Peace University, Pune, Maharashtra
Vaishnavi Mundada
Ayush Chaudhary
School of Computer Engineering & Technology MIT World Peace University, Pune, Maharashtra
School of Computer Engineering & Technology MIT World Peace University, Pune, Maharashtra
Harsh Shelke School of Computer Engineering & Technology MIT World Peace University, Pune, Maharashtra --------------------------------------------------------------------***----------------------------------------------------------------Abstract— Breast cancer is among the main reasons why women die worldwide. Breast cancer mortality rates and treatment expenses can be decreased with early detection and diagnosis. In this effort, we have put forth a novel, affordable, computer vision-based method for detecting and diagnosing breast cancer. Convolutional neural networks are also used for medical image classification. The proposed model is a very simple and cost effective approach with high accuracy and useful outcomes. We have also explored the different challenges faced and the future scope of the project. Keywords—Breast Cancer, Computer Convolutional neural network, Detection
I.
Invasive Ductal Carcinoma (IDC), one of the most prevalent forms of breast cancer, is one that we are finding. There is improvement in the field of diagnosis due to evolving technology. Convolutional neural network is the most widely used machine learning algorithm in the field of medical image analysis [4]. The fundamental reason for this is because CNN exactly fits the two-dimensional structure of the image in structure and uses this spatial relationship as the algorithm's direct input value [4].
II.
vision,
OBJECTIVES
The main objective of this project is to design a computer vision system that can help with early detection of breast cancer. In this project, we have explored computer vision as an image preprocessing technique. Along with it, convolutional neural networks are used for image classification. System architecture is shown below fig.1
INTRODUCTION
Overtaking lung cancer, breast cancer is the most common cancer among women. In India the survival rate for breast cancer patients is about 60% as compared to 90% in the United States, for the last five years [1]. By enhancing treatment options, early detection methods, awareness campaigns, and better diagnostics, we can increase these survival rates. Because of its simplicity and practicability, ultrasound has become a standard tool for diagnosing breast disorders. The findings of B-mode ultrasonography, on the other hand, are related to the level of expertise of doctors, poor image quality, benign presentations of malignant tumors, and visual fatigue or neglect on the part of observers [2]. If a huge number of ultrasound mammary images are manually examined, there will be significant flaws. Misdiagnosis is common when lesions that should be properly diagnosed are missed by radiologists [2]. CALC (calcification), CIRC (circumcised masses), SPIC (speculated masses), MISC (other ill-defined masses), ARCH (architectural distortion), and ASYM (asymmetry) are the six types of breast cancer [3]. In this paper, © 2024, IRJET
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Fig.1 System Architecture
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