Abstract:

Convolutional neural networks (CNNs) are widely used to highlight relevant information and suppress irrelevant information in tasks to be performed for detecting, diagnosing, and monitoring medical diseases. However, reasons such as computational complexity, feature quality problems, and increasing feature size challenge the disease detection performance of CNN models. In recent times, attention mechanisms have been used to enhance the performance of CNN models, helping to overcome challenges. The Convolutional Block Attention Module (CBAM) attention mechanism is a method developed to extract relevant complex features with the help of the modules it contains. The integration of this mechanism with the CNN model significantly improves the performance of the model. This study aims to classify bladder tissue with models created by combining VGGNet and CBAM attention mechanism. Experiments were carried out using accuracy, precision, recall, F1-score, and area under the curve metrics (AUC) to compare the performances of VGGNet and VGGNet+CBAM models. According to the results, the VGG19+CBAM model has demonstrated the highest performance values in terms of accuracy, precision, recall, F1-score, and AUC criteria. The accuracy, precision, recall, F1-score, and AUC values of this model are 0.990, 0.992, 0.984, 0.986, and 0.994, respectively. VGGNet+CBAM models have shown better performance than VGGNet models. Based on the achieved performance values, it is evident that the proposed approach is effective in the diagnosis of bladder tumors.

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