Cervical cancer is one type of gynaecological cancers and the majority of these complications of cervical cancer are associated to human papillomavirus infection. There are numerous risk factors associated with cervical cancer. It is important to recognize the significance of test variables of cervical cancer for categorizing the patients based on the results. This work intended to attain deeper understanding by applying machine learning techniques in R to analyze the risk factors of cervical cancer. Various types of feature selection techniques are explored in this work to determine about important attributes for cervical cancer prediction. Significant features are identified over various iterations of model training through several feature selection methods and an optimized feature selection model has been formed. In addition, this work aimed to build few classifier models using C5.0, random forest, rpart, KNN and SVM algorithms. Maximum possibilities were explored for training and performance evaluation of all the models. The performance and prediction exactness of these algorithms are conferred in this paper based on the outcomes attained. Overall, C5.0 and random forest classifiers have performed reasonably well with comprehensive accuracy for identifying women exhibiting clinical sign of cervical cancer.
Classical-quantum transfer learning is a recent development in the field of quantum computing, which involves the modification of a pre-trained classical network and compounding it with a variational quantum circuit. This paper puts forward a quantum transfer learning-based approach for three different image classification tasks-classifying organic and recyclable from Trash, TB detection from chest X-ray images and detecting the presence of cracks from concrete crack images. The model used in this paper is a concatenation of pre-trained classical feature extractor with a quantum circuit as classifier. This paper compares the classification results obtained using various pre-trained networks such as VGG19, DenseNet169 and AlexNet, as feature extractors. From the obtained results, it is inferred that, DenseNet, Alexnet and VGG19 performs better in trash, TB and crack datasets, respectively. No model is the best for all the classification tasks, it is purely based on parameters such as dataset size, test-train split and learning rate.
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