Prevalence And Risk Factors Of Musculoskeletal Disorders Among Information And Technology Professionals In Chennai District - A Cross Sectional Study

Abstract

Abstract:This paper explores using deep learning algorithms coupled with ultrasonic datasets how to enhance the accuracy and efficiency of automated breast cancer detection. Still a serious global health concern, breast cancer depends on early diagnosis to maximise patient outcomes. In mammography and biopsy diagnosis, conventions have negative effects on patient age, tissue density, and probability of human error. Although ultrasonic imaging is less invasive and more fairly cost-effective, its variation and complexity make appropriate interpretation challenging. Especially convolutional neural networks (CNNs), deep learning has developed into a powerful tool for automating medical picture interpretation, so addressing these challenges.

Emphasising improved diagnosis accuracy and reduction of false positives and negatives, this work investigates the application of deep learning models to ultrasonic images for breast cancer diagnosis. The method uses large, tagged ultrasonic data to teach deep neural networks, therefore enabling both benign and malignant lesion diagnosis. Combining ultrasonic imaging with state-of-the-art deep learning techniques seeks to produce a robust, readily accessible diagnostic system able to support clinicians in real-time informed decisions.

The outcomes of this work reveal truly remarkable gains in automated breast cancer diagnosis enabled by deep learning breakthroughs applied to ultrasonic data. Particularly in regions with limited access to sophisticated imaging technology, the recommended approach has the possibility to reduce healthcare expenses, improve early diagnosis, and raise the availability of diagnostic instruments. This work provides a good alternative for traditional methods, therefore enhancing the accuracy, efficiency, and availability of breast cancer detection and hence the outcomes of healthcare.