Abstract
In recent years, the recognition of images with feature extraction in medical applications is a big challenge. It is a tough task for the Doctors to diagnose the diseases through image recognition with the scanned images or x-ray images. To enhance the image recognition with feature extraction for the medical applications, a novel intelligent system has been developed using artificial neural network. It gives high efficiency in recognizing the image with feature extraction compared over fuzzy logic system. The artificial neural network algorithm was used for the feature extraction from the scanned images of patients. The implementation has been carried out with the help of Tensor flow and Pytorch. The algorithms was tested over 200 sets of scanned images has been utilized for the classification and prediction of trained dataset images. The analysis on the data set and test cases has been performed successfully and acquired 81% of accuracy for the image recognition using artificial neural network algorithm. With the level of significance (p < 0.005), the resultant data depicts the reliability in independent sample t tests. The process of prediction of accuracy for the image recognition, through the ANN gives significantly better performance than the fuzzy logic system.
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Anithaashri, T.P., Rajendran, P.S., Ravichandran, G. (2022). Novel Intelligent System for Medical Diagnostic Applications Using Artificial Neural Network. In: Hemanth, D.J., Pelusi, D., Vuppalapati, C. (eds) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol 101. Springer, Singapore. https://doi.org/10.1007/978-981-16-7610-9_7
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DOI: https://doi.org/10.1007/978-981-16-7610-9_7
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