In this paper, we address the problem of identifying brain haemorrhage which is considered as a tedious task for radiologists, especially in the early stages of the haemorrhage. The problem is solved using a deep learning approach where a convolutional neural network (CNN), the well-known AlexNet neural network, and also a modified novel version of AlexNet with support vector machine (AlexNet-SVM) classifier are trained to classify the brain computer tomography (CT) images into haemorrhage or nonhaemorrhage images. The aim of employing the deep learning model is to address the primary question in medical image analysis and classification: can a sufficient fine-tuning of a pretrained model (transfer learning) eliminate the need of building a CNN from scratch? Moreover, this study also aims to investigate the advantages of using SVM as a classifier instead of a three-layer neural network. We apply the same classification task to three deep networks; one is created from scratch, another is a pretrained model that was fine-tuned to the brain CT haemorrhage classification task, and our modified novel AlexNet model which uses the SVM classifier. The three networks were trained using the same number of brain CT images available. The experiments show that the transfer of knowledge from natural images to medical images classification is possible. In addition, our results proved that the proposed modified pretrained model “AlexNet-SVM” can outperform a convolutional neural network created from scratch and the original AlexNet in identifying the brain haemorrhage.
Face recognition is one of the most popular biometric today. However, there are still challenges in the development of a robust, real-time face recognition system. Several challenges can be listed as poor illumination, rotations of the face and deformations on the face caused by factors like aging. The most frequent deformations on the face are due to facial expressions that indicate the emotional state of the person. A robust face recognition system should perform well under facial expression deformations. In this paper, we focus on this challenge of face recognition and analyzed the performance of the well known feature extractor Local Binary Patterns (LBPs) under varying facial expressions. The facial expressions considered are the six basic expressions which are anger, disgust, fear, happiness, sadness and surprise. The system is tested on BU-3DFE database. The simulation results show that the LBP features form a strong base for expression invariant face recognition and are open to further improvements.
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