An Enhanced Deep Learning Based Model for the Prediction and Diagnosis of Breast Cancer

Anazia Eluemunor Kizito, Chinedu Nkechi Blessing, Chinedu Paschal Uchenna, Ikuenobe Kasimir Ebejale, Diala Leona Concord, Adegher Pascal

Abstract


This article presents an enhanced deep learning-based model for the prediction and diagnosis of breast cancer which has been one of the most serious health concerns worldwide. Traditional diagnostic methods, though effective, often face limitations such as high costs, subjectivity and delays in result interpretation. To address these challenges, this model combines advanced preprocessing techniques with optimized deep learning techniques to extract meaningful features, minimize errors and deliver more reliable results. By leveraging convolutional neural networks (a powerful deep learning approach) the system improves the accuracy speed and consistency of breast cancer detection, making early intervention more achievable and ultimately helping to save lives. The model achieved promising results, with 86% accuracy, precision of 83.5%, a recall of 84 % and an F1-score of 82%, reflecting its diagnostic efficiency. The system was developed using Visual Studio C# for the design of interactive forms, EMGU CV for image processing and Microsoft SQL Server as the backend database. This model is based on Object-Oriented Methodology making it easier to manage and expand. The integration of deep learning in healthcare delivery will assist medical personals to make better decisions and plan treatments that fit individual patients, especially in hospitals with fewer resources.


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