Early polyp detection is vital in avoiding colorectal cancer, a top contributor to cancer-related deaths globally. Automated polyp detection has been greatly improved by deep learning, but extensive deployment tends to be hampered by patient data privacy fears. For this purpose, we suggest a federated learning (FL) paradigm that enables decentralized model training without sharing raw patient data while having high diagnostic accuracy. We first performed an extensive evaluation using DL models on a polyp dataset that we gathered. MobileNetV2 was the best performing model as per important metrics like accuracy, precision, recall, and F1 score. In order to further expand its representational power, we incorporated a transformer module into MobileNetV2 so that the model can better capture long-range dependencies and context information. Our new Transformer-Enhanced MobileNetV2 model was then implemented on several simulated clients in a federated learning scenario. This configuration enabled training over decentralized clinical data without violating patient privacy. We utilized standard FL algorithms for model averaging and evaluated the system in accuracy, precision, recall, F1 score, and convergence time. The devised approach performed extremely well with an accuracy of 98.37% and an F1 score of 0.971 while converging effectively in 27-33 rounds. These findings imply that a promising path for safe and effective medical image analysis is to combine transformer designs with lightweight models in a federated condition.

Fractal Dimension is a basic parameter of fractal geometry and it has been applied in many fields of application including image analysis, texture segmentation, and shape classification. Many fractal dimensions methods have been evolved depending upon different types of images that could be differentiated with greater precision. In this paper, we propose a color approach based on the modified differential box-counting method to estimate fractal dimension of color images in terms of its smoothness. Here we have experimented on four sets of color images like; sixteen number of real natural texture images, eight sets of controlled experimental fabric images with varied color and texture, twelve numbers of generated synthetic images and four smoothed images of known fractal dimension. The results demonstrated that the said proposed method shows accurate fractal dimension estimation of color texture image and also it indicates FD as 2 for smoothed images, which has already been developed in last decade and indicates higher roughness in color images, to check the accuracy of our proposed method, we used a set of twelve synthetic generated images.