Intelligent recognition of colorectal cancer combining application of computer-assisted diagnosis with deep learning approaches

<span>The malignancy of the colorectal testing methods has been exposed triumph to decrease the occurrence and death rate; this cancer is the relatively sluggish rising and has an extremely peculiar to develop the premalignant lesions. Now, many patients are not going to colorectal cancer screening, and people who do, are able to diagnose existing tests and screening methods. The most important concept of this motivation for this research idea is to evaluate the recognized data from the immediately available colorectal cancer screening methods. The data provided to laboratory technologists is important in the formulation of appropriate recommendations that will reduce colorectal cancer. With all standard colon cancer tests can be recognized agitatedly, the treatment of colorectal cancer is more efficient. The intelligent computer assisted diagnosis (CAD) is the most powerful technique for recognition of colorectal cancer in recent advances. It is a lot to reduce the level of interference nature has contributed considerably to the advancement of the quality of cancer treatment. To enhance diagnostic accuracy intelligent CAD has a research always active, ongoing with the deep learning and machine learning approaches with the associated convolutional neural network (CNN) scheme.</span>

Convolutional Neural Network-Based Computer-Assisted Diagnosis of Hashimoto’s Thyroiditis on Ultrasound

Purpose This study investigates the efficiency of deep learning models in the automated diagnosis of Hashimoto’s thyroiditis (HT) using real-world ultrasound data from ultrasound examinations by computer-assisted diagnosis (CAD) with artificial intelligence. Methods We retrospectively collected ultrasound images from patients with and without HT from two hospitals in China between September 2008 and February 2018. Images were divided into a training set (80%) and a validation set (20%). We ensembled nine convolutional neural networks (CNNs) as the final model (CAD-HT) for HT classification. The model’s diagnostic performance was validated and compared from two hospital validation sets. We also compared the accuracy of CAD-HT against seniors/junior radiologists. Subgroup analysis of CAD-HT performance in different thyroid hormone levels, such as hyperthyroidism, hypothyroidism, and euthyroidism, was also evaluated. Results 39280 ultrasound images from 21118 patients were included in this study. The accuracy, sensitivity, specificity of the ensemble HT-CAD model were 0.892, 0.890 and 0.895, respectively. HT-CAD performance between two hospitals was not significantly different. The HT-CAD model achieved a higher performance (P &lt; 0.001) when compared to senior radiologists, with a nearly 9% accuracy improvement. HT-CAD had almost similar accuracy (from 0.871 to 0.894) among the three differences of thyroid hormone level subgroups. Conclusion The HT-CAD strategy based on CNN significantly improved the radiologists’ diagnostic accuracy on HT. Our model demonstrates good performance and robustness in different hospitals and for different thyroid hormone levels.