Deep Learning Models for Medical Imaging (Paperback)

Deep Learning Models for Medical Imaging explains the concepts of Deep Learning (DL) and its importance in medical imaging and/or healthcare using two different case studies: a) cytology image analysis and b) coronavirus (COVID-19) prediction, screening, and decision-making, using publicly available datasets in their respective experiments. Of many DL models, custom Convolutional Neural Network (CNN), ResNet, InceptionNet and DenseNet are used. The results follow 'with' and 'without' transfer learning (including different optimization solutions), in addition to the use of data augmentation and ensemble networks. DL models for medical imaging are suitable for a wide range of readers starting from early career research scholars, professors/scientists to industrialists.

《醫學影像的深度學習模型》解釋了深度學習（DL）的概念以及其在醫學影像和/或醫療保健中的重要性，並使用兩個不同的案例研究進行說明：a）細胞學圖像分析和b）冠狀病毒（COVID-19）預測、篩查和決策，使用各自實驗中的公開可用數據集。在眾多DL模型中，使用了自定義的卷積神經網絡（CNN）、ResNet、InceptionNet和DenseNet。結果包括使用轉移學習（包括不同的優化解決方案）的「有」和「無」，以及使用數據增強和集成網絡。醫學影像的DL模型適用於廣泛的讀者，從初級研究學者、教授/科學家到工業界人士。