Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy.

In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set .The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.

The continuous growth of smart communities and ever-increasing demand of sending or storing videos, have led to consumption of huge amount of data. The video compression techniques are solving this emerging challenge. However, H.264 standard can be considered most notable, and it has proven to meet problematic requirements. The authors present (BPMM) as a novel efficient Intra prediction scheme. We can say that the creation of our proposed technique was in a phased manner; it's emerged as a proposal and achieved impressive results in the performance parameters as compression ratios, bit rates, and PSNR. Then in the second stage, we solved the challenges of overcoming the obstacle of encoding bits overhead. In this research, we try to address the final phase of the (BPMM) codec and to introduce our approach in a global manner through realization of decoding mechanism. For evaluation of our scheme, we utilized VHDL as a platform. Final results have proven our success to pass bottleneck of this phase, since the decoded videos have the same PSNR that our encoder tells us, while preserving steady compression ratio treating the overhead. We aspire our BPMM algorithm will be adopted as reference design of H.264 in the ITU.