Effectiveness of Intravesical Therapy and Neo-Adjuvant Chemotherapy Regimens for Bladder Cancer: A Systematic Review and Network Meta-Analysis

Li, Yan; He, Meizhi; Zhou, Yaoyao; Bian, Xue; Wei, Shuyi; Hong, Yonglan; Yao, Tingyan; Du, Yong

doi:10.2139/ssrn.3335001

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“…According to World Health Organization (WHO) statistics, bladder cancer is one of the most prevalent cancers worldwide with more than 330,000 newly confirmed cases and more than 130,000 deaths every year in the world [1], [2]. This cancer generally occurs three times more often in men than women [3]. Cystoscopy is one of the most reliable methods for diagnosing bladder cancer.…”

Section: Introductionmentioning

confidence: 99%

Cystoscopic Image Classification by Unsupervised Feature Learning and Fusion of Classifiers

et al. 2021

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Cystoscopy imaging is highly recommended for the early diagnosis of bladder cancer, which is the ninth most common cancer in the world. This study presents an intelligent method for classifying cystoscopy images of bladder. In the proposed method, a pre-trained convolutional neural network (CNN) is employed to extract high level semantic features. Then, the number of features is reduced using Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) to avoid curse of dimensionality issue. In the classification phase, an ensemble classifier is constructed by combining Support Vector Machine (SVM), Logistic Regression, Random Forest, and Gaussian Naïve Bayes using weighted majority vote. The proposed method is evaluated on 720 cystoscopy images collected in a medical center. Next, the suggested method is categorized into four different classes including bloody urine, benign masses, malignant masses, and normal cases. The results of the experiments indicated that the presented work achieved an accuracy of 69.02±0.19, which outperformed other competing methods.INDEX TERMS Cystoscopic image classification, ensemble classifier, convolutional neural networks, semantic features, transfer learning.

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Section: Introductionmentioning

confidence: 99%