Auto-outlier Fusion Technique for Chest X-ray classification with Multi-head Attention Mechanism

A chest X-ray is one of the most widely available radiological examinations for diagnosing and detecting various lung illnesses. The National Institutes of Health (NIH) provides an extensive database, ChestX-ray8 and ChestXray14, to help establish a deep learning community for analysing and predicting lung diseases. ChestX-ray14 consists of 112,120 frontal-view X-ray images of 30,805 distinct patients with text-mined fourteen disease image labels, where each image has multiple labels and has been utilised in numerous research in the past. To our current knowledge, no previous study has investigated outliers and multi-label impact for a single X-ray image during the preprocessing stage. The effect of outliers is mitigated in this paper by our proposed auto-outlier fusion technique. The image label is regenerated by concentrating on a particular factor in one image. The final cleaned dataset will be used to compare the mechanisms of multi-head self-attention and multi-head attention with generalised max-pooling.

Machine Learning Performance Analysis to Predict Stroke Based on Imbalanced Medical Dataset

Cerebral stroke, the second most substantial cause of death universally, has been a primary public health concern over the last few years. With the help of machine learning techniques, early detection of various stroke alerts is accessible, which can efficiently prevent or diminish the stroke. Medical dataset, however, are frequently unbalanced in their class label, with a tendency to poorly predict minority classes. In this paper, the potential risk factors for stroke are investigated. Moreover, four distinctive approaches are applied to improve the classification of the minority class in the imbalanced stroke dataset, which are the ensemble weight voting classifier, the Synthetic Minority Over-sampling Technique (SMOTE), Principal Component Analysis with K-Means Clustering (PCA-Kmeans), Focal Loss with the Deep Neural Network (DNN) and compare their performance. Through the analysis results, SMOTE and PCA-Kmeans with DNN-Focal Loss work best for the limited size of a large severe imbalanced dataset,which is 2-4 times outperform Kaggle work.