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A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.
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Paul Sudip, Jain Shruti
Аннотация
BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot.
OBJECTIVE: In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other datasets.
METHODS: In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated.
RESULTS: 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy.
CONCLUSION: The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement over TCIA and BRaTS datasets respectively.
OBJECTIVE: In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other datasets.
METHODS: In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated.
RESULTS: 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy.
CONCLUSION: The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement over TCIA and BRaTS datasets respectively.