The incidence rate for Type 2 Diabetes Mellitus (T2DM) has been increasing over the years. T2DM is a common lifestyle-related disease and predicting its occurrence before five years could help patients to alter their lifestyle ahead and hence prevent T2DM. We intend to investigate the feasibility of radiomics features in predicting the occurrence of T2DM using screening mammography images which could benefit us in terms of the preventability of the disease. This study has examined the prevalence of T2DM using 110 positive samples (developed T2DM after 5 years) and 202 negative samples (did not develop T2DM after five years). The whole breast region was selected as the Region Of Interest (ROI), from which radiomics features were to be extracted. The mask was created from every image using a modified threshold value (by Otsu's binarization method) to obtain a binary image of the breast. 668 radiomics features were then extracted and analyzed using different machine learning algorithms built in the Python programming language such as Random Forest (RF), Gradient Boosting Classifier (GBC), and Light-Gradient Boosting Model (LGBM) as they could give excellent classification and prediction results. A five-fold cross-validation method was carried out; the accuracy, sensitivity, specificity and AUC were calculated when implementing each of the algorithms, and hyperparameter tuning was carried out to tune the models for better performance. The RF and GBC produced good accuracy results (⪆ 70%), but low sensitivity values. LGBM’s accuracy is almost 70% but it has the highest sensitivity (43.9%) and decent specificity (74.4%).
Subarachnoid Hemorrhage (SAH) detection is a critical, severe problem that confused clinical residents for a long time. With the rise of deep learning technologies, SAH detection made a significant breakthrough in recent ten years. Whereas, the performances are significantly degraded on imbalanced data, makes deep learning models have always suffered criticism. In this study, we present a DenseNet-LSTM network with Class-Balanced Loss and the transfer learning strategy to solve the SAH detection problem on an extremely imbalanced dataset. Compared to the previous works, the proposed framework not merely effectively integrate greyscale features the and spatial information from the consecutive CT scans, but also employ Class-Balanced loss and transfer learning to alleviate the adverse effects and broaden feature diversity respectively on an extreme SAH cases scarcity dataset, mimicking the actual situation of emergency departments. Comprehensive experiments are conducted on a dataset, consisted of 2,519 cases without hemorrhage cases and only 33 cases with SAH. Experimental results demonstrate the F-measure score of SAH detection achieved a remarkable improvement, the backbone DenseNet121 gained around 33% promotion after transfer learning, and on this basis, importing the Class-Balanced Loss and the LSTM structure, the F-measure score further increased 6.1% and 2.7% sequentially.
Purpose: The target disorders of emergency head CT are wide-ranging. Therefore, people working in an emergency department desire a computer-aided detection system for general disorders. In this study, we proposed an unsupervised anomaly detection method in emergency head CT using an autoencoder and evaluated the anomaly detection performance of our method in emergency head CT. Methods: We used a 3D convolutional autoencoder (3D-CAE), which contains 11 layers in the convolution block and 6 layers in the deconvolution block. In the training phase, we trained the 3D-CAE using 10,000 3D patches extracted from 50 normal cases. In the test phase, we calculated abnormalities of each voxel in 38 emergency head CT volumes (22 abnormal cases and 16 normal cases) for evaluation and evaluated the likelihood of lesion existence. Results: Our method achieved a sensitivity of 68% and a specificity of 88%, with an area under the curve of the receiver operating characteristic curve of 0.87. It shows that this method has a moderate accuracy to distinguish normal CT cases to abnormal ones. Conclusion: Our method has potentialities for anomaly detection in emergency head CT.
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