A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion

Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be very helpful. In this paper, we propose a novel model, which uses a novel attention mechanism to pinpoint the differences in features across the spatial dimensions and symmetry of the lesion, thereby focusing on the dissimilarities of various classes based on symmetry, uniformity in texture and color, etc. Additionally, to take into account the variations in the boundaries of the lesions for different classes, we employ a gradient-based fusion of wavelet and soft attention-aided features to extract boundary information of skin lesions. We have tested our model on the multi-class and highly class-imbalanced dataset, called HAM10000, and achieved promising results, with a 91.17\% F1-score and 90.75\% accuracy. The code is made available at: https://github.com/AyushRoy2001/WAGF-Fusion.

Fault Area Detection in Leaf Diseases using k-means Clustering

With increasing population the crisis of food is getting bigger day by day.In this time of crisis,the leaf disease of crops is the biggest problem in the food industry.In this paper, we have addressed that problem and proposed an efficient method to detect leaf disease.Leaf diseases can be detected from sample images of the leaf with the help of image processing and segmentation.Using k-means clustering and Otsu's method the faulty region in a leaf is detected which helps to determine proper course of action to be taken.Further the ratio of normal and faulty region if calculated would be able to predict if the leaf can be cured at all.