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Abstract

In the present days, one of the deadly diseases is the brain tumor, which is formed by the expansion of irregulated cells inside the skull or brain. The people's death count increases with the steadily increasing condition. Therefore early diagnosis is significant for the patient to cure the disease and it increases the chance of survival. Multiple algorithms are examined by various researchers for identifying and determining the brain tumor for accurate and fast diagnosis. As the number of patients increased, the amount of information handled also increased. Utilizing the existing technique is more costly and is ineffective for a huge number of patients. Therefore, an advanced deep learning-assisted model is developed for segmentation and also for classifying brain tumors. Initially, the input images needed for this research accumulated from benchmark sites. First, a segmentation procedure is carried out to find out the accurate region where the tumor is present. An Optimized Trans-ResUnet (OTRUnet) approach is implemented to analyze the brain tumor segmentation. In addition, the network attributes are optimized via the “Enhanced Pufferfish Optimization (EPO)” Algorithm. The resultant segmented images are passed to the stage of brain tumor classification. An Atrous Convolution-based Residual DenseNet with a Gated Recurrent Unit layer (ACRD-GRU) is designed to function in the process of categorization for brain tumors. The performance of the designed model is assessed using different statistical indicators like “accuracy, FNR, FPR, precision” to evaluate the categorization capability of the suggested model. Research finding emphasizes that the developed ACRD-GRU model improves the categorization capability by leveraging the strengths of both residual DenseNet and GRU architectures. Resultant analysis proved the effectiveness of the designed model and lowered the possibility of overfitting issues. Numerical values assessments are employed to progress the network's capability for efficient disease categorization.

Keywords

brain tumor classification adaptive segmentation optimized transformer residual Unet enhanced pufferfish optimization atrous convolution-based residual densenet with a gated recurrent unit

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Enhanced segmentation and classification for effective brain tumor diagnosis using atrous convolution based residual densenet with gated recurrent unit layer

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