A novel voting convergent difference neural network for diagnosing breast cancer

DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis

Malaria is a significant health concern worldwide, particularly in Africa where its prevalence is still alarmingly high. Using artificial intelligence algorithms to diagnose cells with malaria provides great convenience for clinicians. In this paper, a densely connected convolutional dynamic learning network (DCDLN) is proposed for the diagnosis of malaria disease. Specifically, after data processing and partitioning of the dataset, the densely connected block is trained as a feature extractor. To classify the features extracted by the feature extractor, a classifier based on a dynamic learning network is proposed in this paper. Based on experimental results, the proposed DCDLN method demonstrates a diagnostic accuracy rate of 97.23%, surpassing the diagnostic performance than existing advanced methods on an open malaria cell dataset. This accurate diagnostic effect provides convincing evidence for clinicians to make a correct diagnosis. In addition, to validate the superiority and generalization capability of the DCDLN algorithm, we also applied the algorithm to the skin cancer and garbage classification datasets. DCDLN achieved good results on these datasets as well, demonstrating that the DCDLN algorithm possesses superiority and strong generalization performance.

A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis

Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying corona virus disease 2019 from other types of pneumonia and normal cases provides clinicians with tremendous conveniences in diagnosis process. In this article, a deep ensemble dynamic learning network is proposed. After a chain of image preprocessing steps and the division of image dataset, convolution blocks and the final average pooling layer are pretrained as a feature extractor. For classifying the extracted feature samples, two-stage bagging dynamic learning network is trained based on neural dynamic learning and bagging algorithms, which diagnoses the presence and types of pneumonia successively. Experimental results manifest that using the proposed deep ensemble dynamic learning network obtains 98.7179% diagnosis accuracy, which indicates more excellent diagnosis effect than existing state-of-the-art models on the open image dataset. Such accurate diagnosis effects provide convincing evidences for further detections and treatments.

Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method

Developing a prediction model from risk factors can provide an efficient method to recognize breast cancer. Machine learning (ML) algorithms have been applied to increase the efficiency of diagnosis at the early stage. This paper studies a support vector machine (SVM) combined with an extremely randomized trees classifier (extra-trees) to provide a diagnosis of breast cancer at the early stage based on risk factors. The extra-trees classifier was used to remove irrelevant features, while SVM was utilized to diagnose the breast cancer status. A breast cancer dataset consisting of 116 subjects was utilized by machine learning models to predict breast cancer, while the stratified 10-fold cross-validation was employed for the model evaluation. Our proposed combined SVM and extra-trees model reached the highest accuracy up to 80.23%, which was significantly better than the other ML model. The experimental results demonstrated that by applying extra-trees-based feature selection, the average ML prediction accuracy was improved by up to 7.29% as contrasted to ML without the feature selection method. Our proposed model is expected to increase the efficiency of breast cancer diagnosis based on risk factors. In addition, we presented the proposed prediction model that could be employed for web-based breast cancer prediction. The proposed model is expected to improve diagnostic decision-support systems by predicting breast cancer disease accurately.

Noninvasive oral cancer screening based on local residual adaptation network using optical coherence tomography

Oral cancer is known as one of the relatively common malignancy types worldwide. Despite the easy access of the oral cavity to examination, the invasive biopsy is still essential for final diagnosis, which requires laborious operation and complicated trained specialists. With the development of deep learning, the artificial intelligence (AI) technique is applied for oral cancer examinations and alleviates the workload of manual screening on biopsy. However, existing computer-aided oral cancer diagnostic methods focus on oral cavity environment photos and histology images, which require complicated operations for doctors and are invasive and painful for patients. As a noninvasive, real-time imaging technique, optical coherence tomography (OCT) can express sufficient identical information for oral cancer screening, but it has not been effectively explored for automatic oral cancer diagnosis. This paper proposes a novel deep learning method named Local Residual Adaptation Network (LRAN) for noninvasive oral cancer screening on OCT images, collected from 25 patients in Beijing Stomatological Hospital. Our proposed LRAN consists of a Residual Feature Representation (RFR) module and a Local Distribution Adaptation (LDA) module. Specifically, RFR firstly adopts stacked residual blocks as the backbone network to learn feature representations for training data, optimized by the Cross-Entropy loss, and then deploy Euclidean distance to measure the distribution distance between training and testing OCT images. Finally, LRAN achieves distribution-gap bridging by the LDA module, which integrates local maximum mean discrepancy constraint to estimate and minimize the distribution discrepancy between training and testing sets within the same category. We also collected an OCT-based oral cancer image dataset to evaluate the effectiveness of the proposed method, and it achieves an accuracy of 91.62%, a sensitivity of 91.66%, and a specificity of 92.58% on this self-collected dataset. Furthermore, we conduct a quantitative and qualitative analysis, and the results demonstrate LRAN model has excellent capability to solve the noninvasive oral cancer screening task.

A bagging dynamic deep learning network for diagnosing COVID-19

COVID-19 is a serious ongoing worldwide pandemic. Using X-ray chest radiography images for automatically diagnosing COVID-19 is an effective and convenient means of providing diagnostic assistance to clinicians in practice. This paper proposes a bagging dynamic deep learning network (B-DDLN) for diagnosing COVID-19 by intelligently recognizing its symptoms in X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, a bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects the feature extractor and bagging classifier in series. Experimental results verify that the proposed B-DDLN achieves 98.8889% testing accuracy, which shows the best diagnosis performance among the existing state-of-the-art methods on the open image set. It also provides evidence for further detection and treatment.