image processing

The application of machine learning technologies for cancer detection purposes are rising due to their ever-increasing accuracy. Melanoma is one of the most common types of skin cancer. Detection of melanoma in the early stages can significantly prevent illness and fetal death. The application of innovative machine learning technology is highly relevant and valuable due to medical practitioners' difficulty in early-stage diagnoses. This paper provides an open-source tutorial on the performance of an algorithm that helps to diagnose melanoma by extracting features from dermatoscopic images and their classification. First, we used a Dull-Razor preprocessing method to remove extra details such as hair. Next, histogram adjustments and lighting thresholds were used to increase the contrast and select lesion boundaries. After using a threshold, a binary-classified version of image was obtained, and the boundary of the lesion was determined. As a result, the features from skin tissue were extracted. Finally, a comparative study was conducted between three methods which are Artificial Neural Network (ANN), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The results show that ANN could achieve better accuracy (83.5%). In order to mitigate the biases in existing studies, the source code of this research is available at hadi-naghavipour.com/ml to serve aspiring researchers for improvement, correction and learning and provide a guideline for technology manager practitioners.

Artificial intelligence (AI) has the potential to revolutionize the field of archaeology by enabling researchers to analyze large amounts of data quickly and accurately. In this article, we have tried to implement some methods and algorithms in image processing on the image of ancient artifacts. We implemented the algorithms on two historical models as examples, one of which is the image of a coin decorated with the image of Farkhan the Great and the other is the coin with the image of Khursheed Daboui to obtain the details of these works from the images on the computer. We used Edge Detection, Hough Transform, imcontour, and Filter Images Using Predefined Filters algorithms in MATLAB software, each of these algorithms is used for specific purposes in image processing. By using digital image analysis techniques, researchers can gain a deeper understanding of the objects and sites they are studying and can make new and important discoveries about the history and culture of ancient civilizations.