Hepatitis C, a particularly dangerous form of viral hepatitis caused by hepatitis C virus (HCV) infection, is a major socio-economic and public health problem. Due to the rapid development of deep learning, it has become a common practice to apply deep learning to the healthcare industry to improve the effectiveness and accuracy of disease identification. In order to improve the effectiveness and accuracy of hepatitis C detection, this study proposes an improved denoising autoencoder (IDAE) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder. However, due to the presence of these features, encoders that directly add random noise may mask certain intrinsic properties of the data, making it challenging to learn deeper features. In this study, the problem of data information loss in traditional denoising autoencoding is addressed by incorporating the concept of residual neural networks into an enhanced denoising autoencoder. In our experimental study, we applied this enhanced denoising autoencoder to the open-source Hepatitis C dataset and the results showed significant results in feature extraction. While existing baseline machine learning methods have less than 90% accuracy and integrated algorithms and traditional autoencoders have only 95% correctness, the improved IDAE achieves 99% accuracy in the downstream hepatitis C classification task, which is a 9% improvement over a single algorithm, and a nearly 4% improvement over integrated algorithms and other autoencoders. The above results demonstrate that IDAE can effectively capture key disease features and improve the accuracy of disease prediction in hepatitis C data. This indicates that IDAE has the potential to be widely used in the detection and management of hepatitis C and similar diseases, especially in the development of early warning systems, progression prediction, and personalized treatment strategies.
Hepatitis, as an important global public health problem, its early diagnosis and precise treatment are crucial to reduce the disease burden and improve patient prognosis. In recent years, with the rapid growth of biomedical data, how to extract valuable information from massive and complex hepatitis-related data has become a major challenge for medical research. Although traditional machine learning techniques have gradually become the core means of mining the deep value of medical big data, and have made breakthroughs in the accurate diagnosis of diseases, prospective prediction of patient treatment response, and formulation of individualized treatment strategies. However, conventional machine learning methods have revealed a series of inherent limitations when applied to complex medical data like hepatitis C. An et al. found that conventional machine learning techniques are difficult to effectively mine the non-linear, high-dimensional pathophysiological patterns hidden in highly complex medical data containing multiple clinical indicators and biomarker information. Meanwhile, Rahman et al. suggested the prevalent category imbalance problem in medical datasets leads traditional machine learning models to be ineffective in dealing with rare and early-stage conditions, and to face significant challenges in terms of robustness and generalization when coping with situations such as high noise, large amounts of missing data, and outliers.
It is in this context that deep learning, as an innovative driver in the field of machine learning, has rapidly penetrated and deepened into medical data analysis in recent years. Traditional models are limited by the complexity, variability, and noise of medical data. Therefore, researchers are increasingly adopting deep learning noise reduction techniques to effectively remove medical data noise and extract key features to improve diagnostic accuracy and clinical decision-making efficiency. For example: DnCNN adopts a residual learning strategy, where the model is dedicated to estimating the residuals of the noisy image relative to its corresponding noiseless original. This innovative approach means that the network only needs to focus on learning the properties of the noisy components, which reduces the difficulty of model training while effectively denoising. Cycle-GAN proposes the use of a discriminator network for distinguishing the real noise-free image from the image generated by the denoising network, by which the denoising network is forced to generate results closer to the real noise-free image for denoising purposes. RED-CNN utilizes a residual learning structure, where the image features are captured by the encoder and reconstructed inversely in the decoder, and the network focuses on learning the residuals between the noisy image and the noise-free image, thus achieving effective noise removal. FFDNet dynamically adapts to different noise levels through a noise level-aware deep convolutional network that applies end-to-end learning to remove image noise. While AE, as an unsupervised deep learning framework derived from neural network theory, its variant DAE has likewise been widely used in medical data denoising and disease detection and classification tasks in recent years. For example, Liu et al. achieved effective extraction of extracted depth features from breast cancer gene expression and CNA data by changing the encoder is realistic dual input denoising autoencoder. Im et al. combined a noise-reducing autoencoder and a variational autoencoder to denoise the data by introducing random noise and optimizing the distribution of hidden variables to learn a robust and interpretive low-dimensional representation of the data during the training process.
However, the application of noise-reducing autoencoders to hepatitis C data has encountered several challenges: firstly, limited by small datasets, noise-reducing autoencoders are prone to simplify the feature overload in a small number of samples, and the deepening of the network may lead to gradient problems and performance degradation due to the high complexity of the features; secondly, the ability of the model to generalize is highly dependent on the type and strength of the added noise; and thirdly, the opacity of the deep learning models limits the intuitive understanding of predictive causal logic, which is critical for medical decision-making. In order to address the above problems and achieve fast convergence of the shallow network while learning more advanced features of the hepatitis C data, we propose to use an improved denoising autoencoder(IDAE), which introduces the concept of ResNet residual neural network in computer vision for compensating the features that are masked by the data itself by the random noise added to the input. The extracted features are finally used for hepatitis C disease detection. This work is expected to provide a powerful data-driven tool for early diagnosis and individualized treatment of hepatitis, as well as provide lessons and insights for deep learning feature extraction research for other chronic diseases The following are the main contributions of this paper:
The article is structured as follows: Related work describes related work on deep learning in the field of liver, and encoders for medical applications. Methods discusses the model used in our work and its structure and included concepts. Experiments and results summarizes our experimental findings and observations and evaluates the model from several perspectives to support the model. Conclusion summarizes the research in this paper.
