Transferring Feature Extractors for Interpretable Cancer Image Classification
سال انتشار: 1400
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 567
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شناسه ملی سند علمی:
ECMCONF05_066
تاریخ نمایه سازی: 29 خرداد 1400
چکیده مقاله:
In recent years, plenty of image datasets have been created and shared from different areas like robotic, medical, and social media. This great plenty of datasets has some challenges. Content based image retrieval is a method to search similar contents for real-time retrieval. Supervised classification methods are accurate, and search-based methods are interpretable for medical experts. In this article, we implement the two methods to use the benefits them. First, the proposed method is trained on an ImageNet dataset with the same image sizes. After that, the network is trained on the textures dataset that its nature is close to the two cancer datasets. Finally, the proposed method is validated based on three criteria using the k-NN classifier. We compare our results with related work, and the results show that the proposed method has better performance than the other method.
کلیدواژه ها:
Content Based Image Retrieval (CBIR) ، Deep Learning ، Medical Image Search ، Medical Image Classification
نویسندگان
Mohammadreza Parvizimosaed
Department of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
Mohammadreza Noei
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran