The early detection of skin cancer can lead to high prognosis rate. Thus it is very important to identify abnormalities in skin as early as possible. However, the detection of abnormalities at their early stages is a challenging task since the shape and colour of the abnormalities vary with different persons. In this study, fractal model for skin cancer diagnosis is developed. Differential Box Counting (DBC) method is implemented to get the fractal dimension from the dermoscopic images from two databases; International Skin Imaging Collaboration (ISIC) and PH 2 database. The fractal features are classified using a parametric and non-parametric classification approach. The system provides promising results for skin cancer diagnosis with 96.5% accuracy on PH 2 images and 91.5% accuracy on ISIC database images using the non-parametric classifier whereas parametric classifier gives 95% (PH 2 ) and 90% (ISIC) images.

People who are visually impaired have a hard time navigating their surroundings, recognizing objects, and avoiding hazards on their own since they do not know what is going on in their immediate surroundings. We have devised a new method of delivering assistance to people who are blind in their quest to improve their vision. An affordable, compact, and easy-to-use Raspberry Pi 3 Model B+ was chosen to demonstrate how the proposed prototype works. All of this is made possible by a camera and sensors and the most modern image processing algorithmic methods available. A camera and ultrasonic sensors are utilized to measure the distance between the user and the object. Using a global positioning system (GPS), it is also possible to track down people who are blind or have impaired vision (GPS). Consider the possibility of making this a for-profit product. Small and light, it may be fitted to a regular pair of spectacles without incurring additional fees or posing any further difficulties.

No abstract

No abstract

No abstract