Subodh Srivastava

Breast cancer continues to pose a major global health challenge, underscoring the need for more effective detection methods to improve patient prognosis. Thermography is becoming popular as a safe, non-invasive way to detect body changes related to tumor growth without using radiation. This study investigates the use of thermography to distinguish between benign and malignant breast lesions, aiding early detection and risk stratification. Thermographic data from patients undergoing screening and diagnostic evaluation will be collected and analyzed using advanced image processing and machine learning to develop a classification model for breast abnormalities. The proposed system aims to integrate thermography into detection protocols to facilitate timely diagnosis. Extensive experiments demonstrate that this method effectively supports identification, localization, and classification of breast cancer, offering potential as a reliable screening tool.

This paper proposes a Mish W-Net deep framework for precise segmentation of the primary heart sounds, S1 and S2, from PCG signals. The proposed deep framework incorporates a synchro-squeezed wavelet transform for signal-to-image conversion. Additionally, a random search-optimized complex diffusion unsharp masking is proposed to mitigate the issues of noise and image quality after the signal-to-noise conversion. Besides this, to achieve self-regularizing feedback learning, the Mish activation function is employed at the architectural level of the proposed Mish W-Net. The effectiveness of the proposed deep framework is evaluated on the publicly available PhysioNet/Computing in Cardiology PCG dataset. Comparative qualitative and quantitative assessment outperforms the existing methodologies. The proposed method achieves accuracy, precision, recall, F1-score, and intersection over union of 98.28%, 98.77%, 99.50%, 99.13%, and 98.28%. It shows an effective framework that offers precise segmentation and detection of heart sounds.