(1) Background: People may be infected with an insect-borne disease (malaria) through the blood input of malaria-infected people or the bite of Anopheles mosquitoes. Doctors need a lot of time and energy to diagnose malaria, and sometimes the results are not ideal. Many researchers use CNN to classify malaria images. However, we believe that the classification performance of malaria parasites can be improved. (2) Methods: In this paper, we propose a novel method (ROENet) to automatically classify malaria parasite on the blood smear. The backbone of ROENet is the pre-trained ResNet-18. We use randomized neural networks (RNNs) as the classifier in our proposed model. Three RNNs are used in ROENet, which are random vector functional link (RVFL), Schmidt neural network (SNN), and extreme learning machine (ELM). To improve the performance of ROENet, the results of ROENet are the ensemble outputs from three RNNs. (3) Results: We evaluate the proposed ROENet by five-fold cross-validation. The specificity, F1 score, sensitivity, and accuracy are 96.68 ± 3.81%, 95.69 ± 2.65%, 94.79 ± 3.71%, and 95.73 ± 2.63%, respectively. (4) Conclusions: The proposed ROENet is compared with other state-of-the-art methods and provides the best results of these methods.

Background: Blood is responsible for delivering nutrients to various organs, which store important health information about the human body. Therefore, the diagnosis of blood can indirectly help doctors judge a person’s physical state. Recently, researchers have applied deep learning (DL) to the automatic analysis of blood cells. However, there are still some deficiencies in these models. Methods: To cope with these issues, we propose a novel network for the multi-classification of blood cells, which is called DLBCNet. A new specifical model for blood cells (BCGAN) is designed to generate synthetic images. The pre-trained ResNet50 is implemented as the backbone model, which serves as the feature extractor. The extracted features are fed to the proposed ETRN to improve the multi-classification performance of blood cells. Results: The average accuracy, average sensitivity, average precision, average specificity, and average f1-score of the proposed model are 95.05%, 93.25%, 97.75%, 93.72%, and 95.38%, accordingly. Conclusions: The performance of the proposed model surpasses other state-of-the-art methods in reported classification results.

Transformer models have demonstrated remarkable success in various fields, but face notable challenges in blood cell classification. These challenges arise primarily from two factors: (1) high computational demands due to the substantial number of parameters, and (2) insufficient labeled data in blood cell datasets, leading to overfitting risks. To address these issues, we introduce BccT, an innovative Transformer-based model specifically designed to optimize the classification of blood cell images. Central to our approach is the Token Fusion module, which intelligently merges similar tokens, thereby enhancing computational efficiency and reducing training overhead without compromising performance. This novel mechanism contrasts with traditional Transformer methods that process each token independently, resulting in significant redundancy. Additionally, we propose the Fixed-Random Classifier to mitigate overfitting risks, particularly in limited-data environments. Unlike conventional classifiers that necessitate constant parameter updates across all layers, our Fixed-Random Classifier comprises only three layers with predominantly static, randomly-initialized parameters, thus reducing dependency on extensive labeled data. Extensive experiments validate the effectiveness of our model. Using ViT_base as the backbone, our model achieves similar accuracy in half the training time compared to the ViT_base model. In experiments on four public blood cell datasets, our model improves accuracy by approximately 4.5% compared to other state-of-the-art methods.

COVID-19 has caused over 6.35 million deaths and over 555 million confirmed cases till 11/July/2022. It has caused a serious impact on individual health, social and economic activities, and other aspects. Based on the gray-level co-occurrence matrix (GLCM), a four-direction varying-distance GLCM (FDVD-GLCM) is presented. Afterward, a five-property feature set (FPFS) extracts features from FDVD-GLCM. An extreme learning machine (ELM) is used as the classifier to recognize COVID-19. Our model is finally dubbed FECNet. A multiple-way data augmentation method is utilized to boost the training sets. Ten runs of tenfold cross-validation show that this FECNet model achieves a sensitivity of 92.23 ± 2.14, a specificity of 93.18 ± 0.87, a precision of 93.12 ± 0.83, and an accuracy of 92.70 ± 1.13 for the first dataset, and a sensitivity of 92.19 ± 1.89, a specificity of 92.88 ± 1.23, a precision of 92.83 ± 1.22, and an accuracy of 92.53 ± 1.37 for the second dataset. We develop a mobile app integrating the FECNet model, and this web app is run on a cloud computing-based client–server modeled construction. This proposed FECNet and the corresponding mobile app effectively recognize COVID-19, and its performance is better than five state-of-the-art COVID-19 recognition models.

Background: Breast mass is one of the main symptoms of breast cancer. Effective and accurate detection of breast masses at an early stage would be of great value for clinical breast cancer analysis. Methods: We developed a novel mass detection framework named GFNet. The GFNet is comprised of three modules, including patch extraction, feature extraction, and mass detection. The developed breast mass detection framework is of high robustness and generality that can be self-adapted to images collected by different imaging devices. The patch-based detection is deployed to improve performance. A novel feature extraction technique based on gradient field convergence features (GFCF) is proposed to enhance the information of breast mass and, therefore, provide useful information for the following patch extraction module. A novel false positives reduction method is designed by combining the texture and morphological features in breast mass patch. This is the first attempt at fusing morphological and texture features for breast mass false positive reduction. Results: Compared to other state-of-the-art methods, the proposed GFNet showed the best performance on CBIS-DDSM and INbreast with an accuracy of 0.90 at 2.91 false positive per image (FPI) and 0.99 at only 0.97 FPI, respectively. Conclusions: The GFNet is an effective tool for detecting breast mass.