SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection

Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ a pre-trained ResNet-18 embedded with the spatial attention mechanism as the backbone model. Three randomized network models are trained for prediction in the SAFNet, which are fused by majority voting to produce more accurate results. A public ultrasound image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breast cancer classification methods. Therefore, our SAFNet is an accurate tool to detect breast cancer that can be applied in clinical diagnosis

A cerebral microbleed diagnosis method via FeatureNet and ensembled randomized neural networks

Cerebral microbleed (CMB) is a type of biomarker, which is related to cerebrovascular diseases. In this paper, a novel computer aided diagnosis method for CMB detection was presented. Firstly, sliding neighborhood algorithm was used to generate CMB and non-CMB samples from brain susceptibility weighted images. Then, a 15-layer proposed FeatureNet was trained for extracting features from the input samples. Afterwards, structure after the first fully connected layer in FeatureNet was replaced by three randomized neural networks for classification: Schmidt neural network, random vector functional-link net, and extreme learning machine, and the weights and biases in early layers of FeatureNet were frozen during the training of those three classifiers. Finally, the output of the three classifiers was ensemble by majority voting mechanism to get better classification performance. In our experiment, five-fold cross validation was employed for evaluation. Results revealed that our FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM yielded accuracy of 98.22%, 98.23%, and 97.54%, respectively, and the ensembled FeatureNet-EN improved the accuracy to 98.60%, which outperformed several existing state-of-the-art approaches. The proposed FeatureNet-EN model could provide accurate CMB detection, and thus reduce death tolls. Impact Statement — We propose a 15-layer FeatureNet to detect cerebral microbleed (CMB). We propose three FeatureNet variants: FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM. We use ensemble learning to combine three FeatureNet variants, and generate a FeatureNet-EN. The proposed FeatureNet-SNN, FeatureNet-RVFL and FeatureNet-ELM yielded accuracy of 98.22%, 98.23%, and 97.54%, respectively, and the ensembled FeatureNet-EN improved the accuracy to 98.60%, better than state-of-the-art methods. This method could provide accurate CMB detection, and thus reduce death tolls

PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors

TBNet: a context-aware graph network for tuberculosis diagnosis

Background and objectiveTuberculosis (TB) is an infectious bacterial disease. It can affect the human lungs, brain, bones, and kidneys. Pulmonary tuberculosis is the most common. This airborne bacterium can be transmitted with the droplets by coughing and sneezing. So far, the most convenient and effective method for diagnosing TB is through medical imaging. Computed tomography (CT) is the first choice for lung imaging in clinics because the conditions of the lungs can be interpreted from CT images. However, manual screening poses an enormous burden for radiologists, resulting in high inter-observer variances. Hence, developing computer-aided diagnosis systems to implement automatic TB diagnosis is an emergent and significant task for researchers and practitioners. This paper proposed a novel context-aware graph neural network called TBNet to detect TB from chest CT imagesMethodsTraditional convolutional neural networks can extract high-level image features to achieve good classification performance on the ImageNet dataset. However, we observed that the spatial relationships between the feature vectors are beneficial for the classification because the feature vector may share some common characteristics with its neighboring feature vectors. To utilize this context information for the classification of chest CT images, we proposed to use a feature graph to generate context-aware features. Finally, a context-aware random vector functional-link net served as the classifier of the TBNet to identify these context-aware features as TB or normalResultsThe proposed TBNet produced state-of-the-art classification performance for detecting TB from healthy samples in the experimentsConclusionsOur TBNet can be an accurate and effective verification tool for manual screening in clinical diagnosis.</div