DPSF: a Novel Dual-Parametric Sigmoid Function for Optical Coherence Tomography Image Enhancement

LTF-NSI: a novel local transfer function based on neighborhood similarity index for medical image enhancement

Medical image is an essential tool used in quantitative and qualitative evaluation of different diseases. Medical imaging methods such as fluorescein angiography (FA), optical coherence tomography angiography (OCTA), computed tomography (CT), optical coherence tomography (OCT), and X-ray are used for diagnosis. These imaging modalities suffer from low contrast, which leads to deterioration in the image quality. Consequently, this causes limitation in the usage of medical images in clinical routine and hindered its potential by depriving clinicians from assessing useful information that are needed in disease monitoring, treatment, progression, and decision-making. To overcome this limitation, we propose a novel local transfer function for medical image enhancement algorithm using the pixel neighborhood constraint. The proposed algorithm uses block-wise intensity distribution to generate the regional similarity index. The regional similarity index transformed each centered pixel in the block, to generate a new similarity image. An intuitive optimization algorithm is utilized to optimize the proposed algorithm parameters. Experimentation results show that the proposed LTF-NSI performs better than the state-of-the-art methods and improves the interpretability and perception of the medical images, which can provide clinicians and computer vision program with good quantitative and qualitative information.

Artificial intelligence model driven by transfer learning for image-based medical diagnosis

Artificial intelligent (AI) systems for clinical-decision support are an important tool in clinical routine. It has become a crucial diagnostic tool with adequate reliability and interpretability in disease diagnosis and monitoring. Undoubtedly, these models are faced with insufficient data challenges for training, which often directly determines the model’s performance. In order word, insufficient data for model training leads to inefficiency in the model built. To overcome this problem, we propose an AI-driven model by transfer learning in accurate diagnosis for medical decision support. Our approach leverages the shortage of data with a pretrained model by training the neural network with a fraction of the new dataset. For this purpose, we utilized the VGG19 network as the backbone network to support our model in integrating known features with the newly learned features for accurate diagnosis and decision making. Integrating this trained model speeds up the training phase and improve the performance of the proposed model. Experimental results show that the proposed model is effective and efficient in diagnosing different medical diseases. As such, we anticipated that this diagnosis tool will ultimately aid in facilitating early treatment of these treatable diseases, which will improve clinical out-comes.