Abstract:
Skin diseases are more common than other disease in Bangladesh. Skin diseases may be
caused by fungal infection, bacteria, hot weather, allergy, or viruses, etc. The thesis paper
on skin disease detection focuses on the application of advanced technologies, specifically
artificial intelligence and machine learning, for the early and accurate identification of
various skin conditions. The research involves the acquisition of skin images through
diverse sources such as smartphones, cameras, or specialized devices. These images
undergo a systematic process, including image preprocessing, feature extraction, and
classification, facilitated by sophisticated algorithms. There are various types of algorithm
which using in disease detection method. This algorithm is, Convolutional Neural
Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), Transfer
Learning (Pre-trained CNNs), K-Nearest Neighbors (KNN), Feature Extraction
Techniques (e.g., HOG, GLCM, LBP) and Recurrent Neural Networks (RNNs).
Determining the "best" algorithm for skin disease detection depends on various factors,
including the nature of the dataset, the specific skin conditions targeted, and the
characteristics of the images involved. Normally we decide to use CNN algorithm because
of, CNNs offer many advantages for skin disease detection, it's essential to consider the
specific requirements and constraints of the application when choosing an algorithm. The
proposed CNN architecture is based on VGG-16 and will be trained and tested on datasets
collected from skin disease. The goal is to create a reliable and non-invasive method for
diagnosing skin conditions, with potential applications in both self-assessment tools for
users and remote consultations with healthcare professionals.
