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Automatic segmentation of early esophagus cancer (EEC) in gastrointestinal endoscopy (GIE) images is a critical and challenging task in clinical settings, which relies primarily on labor-intensive and time-consuming routines. EEC has often been diagnosed at the late stage since early signs of cancer are not obvious, resulting in low survival rates. This work proposes a deep learning approach based on the U-Net++ method to segment EEC in GIE images. A total of 2690 GIE images collected from 617 patients at the Digestive Endoscopy Center, West China Hospital of Sichuan University, China, have been utilized. The experimental result shows that our proposed method achieved promising results. Furthermore, the comparison has been made between the proposed and other U-Net-related methods using the same dataset. The mean and standard deviation (SD) of the dice similarity coefficient (DSC), intersection over union (IoU), precision (Pre), and recall (Rec) achieved by the proposed framework were DSC (%) ​= ​94.62 ​± ​0.02, IoU (%) ​= ​90.99 ​± ​0.04, Pre ​(%) = ​94.61 ​± ​0.04, and Rec (%) ​= ​95.00 ​± ​0.02, respectively, outperforming the others. The proposed method has the potential to be applied in EEC automatic diagnoses.

This paper presents the formulation of anti-mosquito soap from indigenous lye prepared from plantain peels ash. Neem oil is employed as the key repellent in this formulation. The study investigates the mosquito repelling power of the formulated soaps. The plantain peels ash was found to contain 9.72% of sodium and 76.93% of potassium, indicating its suitability for soap formulation purposes. Out of the 40 individuals supplied with the formulated soaps to measure the mosquito repelling power, 19 individuals (47.5%) reported 4 hours protection from mosquito sting, 8 individuals (20.0%) reported 3 hours protection from a mosquito bite. The results obtained from this study will inform local soap manufactures and the cosmetic industries to consider soap production with mosquito-repelling properties to lessen the possibility of people being bitten by mosquitoes which may result in malaria.

Background: Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians’ burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets. Limited labeled data also hinder trained models’ generalizability under fully supervised learning for computer-aided diagnosis (CAD) systems. Methods: This work proposes a generative adversarial learning-based semi-supervised segmentation framework for GI lesion diagnosis in endoscopic images to tackle the challenge of limited annotations. The proposed approach leverages limited annotated and large unlabeled datasets in the training networks. We extensively tested the proposed method on 4880 endoscopic images. Results: Compared with current related works, the proposed method validates better results (Dice similarity coefficient = 89.42 ± 3.92, Intersection over union = 80.04 ± 5.75, Precision = 91.72 ± 4.05, Recall = 90.11 ± 5.64, and Hausdorff distance = 23.28 ± 14.36) on the challenging multi-sited datasets, confirming the effectiveness of the proposed framework. Conclusion: We explore a semi-supervised lesion segmentation method to employ the full use of multiple unlabeled endoscopic images to improve lesion segmentation accuracy. Experimental results confirmed the potential of our method and outperformed promising results compared with the current related works. The proposed CAD system can minimize diagnostic errors.

Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.