A novel Skin lesion prediction and classification technique: ViT-GradCAM.

BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS: In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT: The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION: This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

Antibiotics and Resistance Genes in Awash River Basin, Ethiopia.

Among contaminants of emerging concern in the environment, a growing attention has been given to antibiotics and antibiotic-resistant genes (ARGs) due to the rise in their usage and potential ecotoxicological and public health effect. However, the occurrence of these contaminants in the environment is little investigated in developing countries particularly in sub-Saharan regions. In this study, the occurrence of three groups of antimicrobials including tetracycline, sulfonamides and fluoroquinolone, and their corresponding ARGs were investigated in the sediments of Awash River Basin, Ethiopia. Out of twelve studied compounds, sulfadiazine and enrofloxacin showed the highest and lowest detection frequency, respectively. Polymerase chain reaction (PCR) analysis revealed that tetA and tetB occurred in all the samples. The relative abundance of the resistant genes was in the following order: tetA > tetB > sul2 > sul1. Redundancy analysis result indicated that some sediment characteristics were found to have influence on the distribution sul1-resistant gene.