Designing a High-performance Deep Learning Theoretical Model for Biomedical Image Segmentation by using Key Elements of the Latest U-Net-Based Architectures: A Recent Study

Current developments in machine learning, particularly related to deep learning, are proving instrumental in identification, and quantification of patterns in the medical images. The pivotal point of these advancements is the essential capability of the deep learning approaches to obtain hierarchical feature representations directly from the images, which in turn is eliminating the need for handcrafted features. Deep learning is expeditiously turning into the state-of-the-art for medical image processing and has resulted in performance improvements in diverse clinical applications.

We aim to create a diagnostic method, optimized by the use of deep learning (DL) and validated by a controlled clinical trial, randomized, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We want to create a high-performance deep learning model for medical picture segmentation that is independent of the type of organs/tissues, dimensions, or image type (2D/3D) and validate it in a randomised, controlled clinical trial using U-Net-based architectures. We largely employed U-Net-based architecture analysis as a technique to identify the major features that we thought crucial in the design and optimization of the integrated DL model, based on the U-Net-based architectures that we imagined. Second, we'll use a randomised, controlled clinical trial to validate the DL model's performance. Our DL model will be a highly automated tool for diagnosing and staging precancers, cervical cancer, and thyroid cancer. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will develop a complete computer-assisted diagnosis technique that will be validated through a randomised controlled experiment. The model will be a highly automated diagnostic and staging tool for precancers, cervical cancer, and thyroid cancer. This would help specialists save time and effort when analysing medical images, aid in the development of a better therapeutic strategy, and provide a "second opinion" on computer-assisted diagnosis.