Brain tumor classification is highly effective in identifying and diagnosing the exact location of the tumor in the brain. The medical imaging system reported that early diagnosis and classification of the tumor increases the life of the human. Among various imaging modalities, magnetic resonance imaging (MRI) is highly used by clinical experts, as it offers contrast information of brain tumors. An effective classification method named fractional-chicken swarm optimization (fractional-CSO) is introduced to perform the severity-level tumor classification. Here, the chicken swarm behavior is merged with the derivative factor to enhance the accuracy of severity level classification. The optimal solution is obtained by updating the position of the rooster, which updates their location based on better fitness value. The brain images are pre-processed and the features are effectively extracted, and the cancer classification is carried out. Moreover, the severity level of tumor classification is performed using the deep recurrent neural network, which is trained by the proposed fractional-CSO algorithm. Moreover, the performance of the proposed fractional-CSO attained better performance in terms of the evaluation metrics, such as accuracy, specificity and sensitivity with the values of 93.35, 96 and 95% using simulated BRATS dataset, respectively.
The ability to use computer and human based interaction would make things easier for the user, but would be challenging for the researchers. More sophisticated imaging systems can handle inter plot the results of image analysis and describe the various objects and their connections in the scene. The electricity, gas, and water metering instruments may employ the Automatic meter reading (AMR) system for automation of bill generation process. The image processing implementation may ease the process of AMR. In this paper, we discussed the image processing applications in AMR.
Contribution/Originality:This study is one of very few studies which have investigated the applications of image processing in automatic meter reading.
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