Özet:

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.

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