Test cases are one of the most important assets in the testing process. This paper presents the testing ontology based SWEBOK and software quality model. The management and retrieval of test cases will play a vital role in test cases reuse. The keyword-based, as well as facet-based retrieval cannot meet user's flexible query requirement because of lack of semantic information. SWEBOK provides a broad agreement on the content of the software engineering discipline. At last this paper discusses the management and retrieval of test cases based on the semantic similarity of two test concepts in two ontologies according to difference sets of super concept, sub concept, extension, and intension.
Skin cancers are one of the most common cancers in the world. Early detections and treatments of skin cancers can greatly improve the survival rates of patients. In this paper, a skin lesions classification system is developed with deep convolutional neural networks of ResNet50, which
may help dermatologists to recognize skin cancers earlier. We utilize the ResNet50 as a pre-trained model. Then, by transfer learning, it is trained on our skin lesions dataset. Image preprocessing and dataset balancing methods are used to increase the accuracy of the classification model.
In classification of skin diseases, our model achieves an overall accuracy of 83.74% on nine-class skin lesions. The experimental results show an impressive effect of the ResNet50 model in finegrained skin lesions classification and skin cancers recognition.
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