Optimized Feature Extraction and Spatial-Temporal Analysis for Accurate Intracranial Hemorrhage Classification

Abstract

Intracranial hemorrhage (ICH) is a critical condition requiring timely and accurate diagnosis, especially in craniocerebral trauma. This chapter presents two deep learning-based models for classifying ICH in CT images. The first employs a convolutional neural network (CNN) for spatial feature extraction and classification via SoftMax or Sigmoid layers. The second proposes a hybrid approach, combining an autoencoder for unsupervised feature extraction with a gradient boosting machine (GBM) for classification. The autoencoder compresses image data to extract essential features, which are classified by GBM through ensemble learning. This chapter outlines the models' design, data preprocessing, training strategies, and evaluation metrics. The aim is to enhance classification accuracy through CNN-based spatial-temporal modeling and efficient feature extraction via autoencoder-GBM. The chapter also explores the hybrid model's potential to outperform traditional CNNs and its feasibility in clinical use.