The ever-growing population of Bangladesh and the lack of appropriate information might prompt an expanded number of patients with malignancy, which is inevitable, and a populace-based study shows that the essential obstruction to the early identification of BCa in Bangladeshi women is an absence of comprehension of screening to identify the beginning phase of the disease. Finally, a web interface has been made to make this model usable for non-technical personals. This work primarily contributes to identifying the usefulness of multi-headed CNN when working with two different types of data inputs. 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% (☒) with a Mean Squared Error (MSE) loss of 0.05. 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. Validation tests were accomplished for quantitative outcomes utilizing the exhibition measures for each procedure. ![]() The relevant dataset comprises grayscale and masked ultrasound images of diagnosed patients. 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. Several analytical techniques, such as Breast MRI, X-ray, Thermography, Mammograms, Ultrasound, etc., are utilized to identify it. ![]() ![]() Though it can occur in all kinds of people, it is undeniably more common in women. Breast cancer is one of the most widely recognized diseases after skin cancer.
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