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Image segmentation is critical in medical image processing for lesion detection, localisation, and subsequent diagnosis. Currently, computer-aided diagnosis (CAD) has played a significant role in improving diagnostic efficiency and accuracy. The segmentation task is made more difficult by the hazy lesion boundaries and uneven forms. Because standard convolutional neural networks (CNNs) are incapable of capturing global contextual information, adequate segmentation results are impossible to achieve. We propose a multiscale feature fusion network (MTC-Net) in this paper that integrates deep separable convolution and self-attentive modules in the encoder to achieve better local continuity of images and feature maps. In the decoder, a multi-branch multi-scale feature fusion module (MSFB) is utilized to improve the network's feature extraction capability, and it is integrated with a global cooperative aggregation module (GCAM) to learn more contextual information and adaptively fuse multi-scale features. To develop rich hierarchical representations of irregular forms, the suggested detail enhancement module (DEM) adaptively integrates local characteristics with their global dependencies. To validate the effectiveness of the proposed network, we conducted extensive experiments, evaluated on the public datasets of skin, breast, thyroid and gastrointestinal tract with ISIC2018, BUSI, TN3K and Kvasir-SEG. The comparison with the latest methods also verifies the superiority of our proposed MTC-Net in terms of accuracy. Our code on https://github.com/gih23/MTC-Net. [ABSTRACT FROM AUTHOR]

Background: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effective and accurate method for early detection. Due to the poor contrast, noise and artifacts which results in difficulty for radiologists to diagnose, Computer-Aided Diagnosis (CAD) systems are hence developed. The extraction of breast region is a fundamental and crucial preparation step for further development of CAD systems.Objective: The proposed method aims to extract breast region accurately from mammographic images where noise is suppressed, contrast is enhanced and pectoral muscle region is removed.Methods: This paper presents a new deep learning-based breast region extraction method that combines pre-processing methods containing noise suppression using median filter, contrast enhancement using CLAHE and semantic segmentation using Deeplab v3+ model.Results: The method is trained and evaluated on mini-MIAS dataset. It has also been evaluated on INbreast dataset. The results outperform those generated by other recent researches and are indicative of the capacity of the model to retain its accuracy and runtime advantage across different databases with different image resolutions.Conclusions: The proposed method shows state-of-the-art performance at extracting breast region from mammographic images. Wide range of evaluation on two commonly used mammography datasets proves the ability and adaptability of the method. [ABSTRACT FROM AUTHOR]