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

Abstract

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.

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

“…We believed that the proposed algorithm is useful in medical feature enhancement [41], provide better input for other automated image processing techniques [42,43] and disease monitoring [44]. In addition, we anticipate that the proposed LTF-NSI will be a powerful tool for clinical diagnosis and disease monitoring.…”