Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling

Abstract

Breast cancer is one of the most widely recognized diseases after skin cancer. Though it can occur in all kinds of people, it is undeniably more common in women. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. In this study, artificial intelligence was used to rapidly detect breast cancer by analyzing ultrasound images from the Breast Ultrasound Images Dataset (BUSI), which consists of three categories: Benign, Malignant, and Normal. The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. The proposed framework is discovered to be effective, substantiating outcomes with only raw image evaluation giving a 78.97% test accuracy and masked image evaluation giving 81.02% test precision, which could decrease human errors in the determination cycle. Additionally, our described framework accomplishes higher accuracy after using multi-headed CNN with two processed datasets based on masked and original images, where the accuracy hopped up to 92.31% (±2) with a Mean Squared Error (MSE) loss of 0.05. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. Finally, a web interface has been made to make this model usable for non-technical personals.

The efficacy of machine learning models in lung cancer risk prediction with explainability

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Smart City Based on Open Data: A Survey

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“…The main objective is to achieve a better detection rate without relying on a traditional single-channel CNN. This method has been proven to work well with less computational power and fewer epochs on medical image datasets 15 . The rest of the paper is divided into multiple sections as literature review in " Literature review " section, materials and methods in " Materials and methods " section with three subsections: dataset description in Dataset description , image pre-processing in " Pre-processing of image dataset " and working procedure in " Working procedure ", result analysis in " Result analysis " section, and conclusion in " Conclusion " section.…”