A Semiautomated Deep Learning Approach for Pancreas Segmentation

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

Fatty infiltration in pancreas leading to steatosis is a major risk factor in pancreas transplantation. Hematoxylin and eosin (H and E) is one of the common histological staining techniques that provides information on the tissue cytoarchitecture. Adipose (fat) cells accumulation in pancreas has been shown to impact beta cell survival, its endocrine function and pancreatic steatosis and can cause non-alcoholic fatty pancreas disease (NAFPD). The current automated tools (E.g. Adiposoft) available for fat analysis are suited for white fat tissue which is homogeneous and easier to segment unlike heterogeneous tissues such as pancreas where fat cells continue to play critical physiopathological functions. The currently, available pancreas segmentation tool focuses on endocrine islet segmentation based on cell nuclei detection for diagnosis of pancreatic cancer. In the current study, we present a fat quantifying tool, Fatquant, which identifies fat cells in heterogeneous H and E tissue sections with reference to diameter of fat cell. Using histological images from a public database, we observed an intersection over union of 0.797 to 0.962 and 0.675 to 0.937 for manual versus Fatquant analysis of pancreas and liver, respectively.

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.