Abstract
This study focuses on the development of a breast cancer prediction model using convolutional neural networks (CNN), a powerful deep learning algorithm capable of learning complex patterns from medical images. The proposed model utilizes a dataset comprising mammography images of patients, including both benign and malignant cases. The images are preprocessed and augmented to enhance the model's ability to generalize. The results demonstrate the effectiveness of the CNN model in accurately predicting breast cancer from mammography images. The model achieves high accuracy, indicating its potential as a valuable tool for breast cancer diagnosis. Furthermore, it shows robust performance in differentiating between benign and malignant cases. The developed CNN-based breast cancer prediction model has the potential to assist radiologists in improving the accuracy and efficiency of breast cancer detection. The integration of deep learning algorithms in healthcare systems can aid in early diagnosis, reduce unnecessary interventions, and ultimately improve patient outcomes.
TopMotivations
Women are affected by breast cancer all across the world, which is a prevalent and life-threatening disease. In 2020, there were 2.3 million women suffering from breast cancer and 685000 deaths globally. As of the end of 2020, there were 7.8 million women alive who were diagnosed with breast cancer in the past 5 years, making it the most prevalent cancer globally. Early detection is essential for improving treatment outcomes and reducing mortality. However, existing screening methods are limited, causing missed diagnoses and delayed treatment. The goal of this project is to develop a highly accurate and efficient breast cancer prediction model using convolutional neural networks (CNNs). It is motivated by the desire to provide medical professionals with a robust tool for early detection and intervention of breast cancer. Aim of the project is to contribute to the medical field, reduce mortality rates, and ultimately make a strong impact on the lives of individuals and their families affected by breast cancer (Sharma, R., 2019)[15].
Project Objectives
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Public Health Planning: The project analyzes large datasets to identify patterns and trends in breast cancer incidence and risk factors. This information helps in designing targeted interventions, awareness campaigns, and screening programs for early detection and prevention, contributing to public health planning.
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Decision support: The project aims to develop a decision support system for healthcare professionals. It assists in making informed decisions about diagnostic tests, preventive measures, and treatment plans based on the predicted risk levels of individual patients
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Prediction Accuracy: The objective is to build a reliable predictive model that accurately predicts the probability of developing breast cancer within a certain timeframe. This enables healthcare professionals to provide personalized recommendations and interventions for individuals at higher risk.
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Risk Assessment: The project seeks to evaluate the risk factors linked to breast cancer by analyzing diverse factors, including family history, age, hormonal influences, lifestyle choices, genetic predisposition, and other pertinent medical data.
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Early Detection: Timely detection of breast cancer greatly enhances treatment effectiveness and increases survival rates. The project's objective is to develop a predictive model that can effectively identify individuals at a higher risk of developing breast cancer in the future.
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To optimize the model's performance, various hyper-parameters, such as the number of layers, filter sizes, and learning rates, are tuned using techniques like grid search or random search. The model's training process employs backpropagation and gradient descent to iteratively update the network's weights and biases.
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To evaluate the model's performance, standard metrics like accuracy, precision, recall, and F1-score are computed. Additionally, the receiver operating characteristic (ROC) curve and area under the curve (AUC) are calculated to assess the model's discrimination ability.
Key Terms in this Chapter
Deep Learning: Deep learning is an advanced branch of artificial intelligence (AI) that aims to create intelligent computer systems capable of learning and decision-making. It draws inspiration from the human brain's structure and function, particularly its network of interconnected neurons. By utilizing artificial neural networks with multiple layers, deep learning algorithms can analyze vast amounts of data, identify meaningful patterns, and make predictions.
Risk Assessment: Risk assessment is a systematic process used to identify, evaluate, and prioritize potential risks or hazards associated with a particular situation, activity, or project. It involves gathering and analyzing relevant data to determine the likelihood of specific risks occurring and the potential impact they may have.
Hyperparameter Tuning: Hyperparameter tuning is a critical process in machine learning that involves selecting the optimal values for the hyperparameters of a model. Hyperparameters are parameters that are set before the learning process begins and directly influence the model's performance and behavior.
Machine Learning: Machine learning is a branch of artificial intelligence that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed. It involves the use of statistical techniques and computational models to analyze and interpret vast amounts of data.s
Convolutional Neural Network (CNN): A Convolutional Neural Network (CNN) is an advanced deep learning algorithm used for image and video recognition. It mimics the human visual processing system, extracting key features through convolutional and pooling layers.
Mammography: Mammography is a vital diagnostic imaging technique used for the early detection and screening of breast cancer. It involves the use of low-dose X-rays to create detailed images of the breast tissue.