Accurate bladder cancer diagnosis using ensemble deep leaning.

There are an estimated 1.3 million cases of cancer globally each year, making it one of the most serious types of urinary tract cancer. The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. In clinical practice, invasive biopsy followed by histological examination continues to be the gold standard for diagnosis. Bladder cancer biomarkers have been used in expensive diagnostic tests created recently, however their reliability is limited by their high rates of false positives and false negatives. The potential and use of artificial intelligence in urological diseases have been the subject of several research, as interest in artificial intelligence in medicine has grown recently. In this paper, a new bladder cancer model called Ensemble Deep Learning (EDL) will be provided to accurately diagnose patients. Outlier rejection is used to filter data using the interquartile range (IQR) then the image diagnosis. The proposed EDL consists of three deep learning algorithms, which are; Convolutional Neural Network (CNN), Generative Adversarial Network (GAN), and a new deep learning method called Explainable Deep Learning (XDL) that depends on Guided Gradient Weighted Class Activation Map (Guided Grad-CAM). In fact, Guided Grad-CAM enables doctor to understand the diagnose. A new voting mechanism will be used to integrate the results of all three methods to produce the final result to accurately diagnose bladder cancer cases. In fact, the used voting method depends on using majority voting based on two different scenarios according to the results of CNN, GAN, and XDL. If these three methods give the same class category, then the final diagnosis will be this class category. On the other hand, if the three methods give different class category, then the final result will be followed by the accuracy of each class. The proposed EDL model was tested after several trials. The results have proved that EDL model is more efficient and more accurate to diagnose bladder cancer disease. It introduced the highest accuracy results and the lowest error results as well as execution time.