Brain Tumor Image Processing Using Fine-Tuned Resnet-101 Classification Model

Medical image processing relies heavily on the diagnosis of brain tumor images. It aids doctors in determining the correct diagnosis and management. One of the primary imaging methods for studying brain tissue is MR imaging. In recent years, deep learning techniques have shown significant potential in image processing. However, the modest quantity of medical images is a restriction of the classification of medical images. As a result of this restriction, fewer medical photos are available. Fine-tuned ResNet-101 (FR-101) is proposed to classify the brain tumor images to counteract this issue. Weiner filter is used to de-noise the acquired raw MR images, and the adaptive histogram equalization technique is used to improve contrast. A stacked autoencoder is utilized in the segmentation procedure to separate the tumor from healthy brain parts from the preprocessed data. The marker-based watershed technique is used to identify the tumor location and structure in the segmented data. The recommended approach is then used in the classification stage. To obtain the highest level of accuracy for our research, accuracy, precision, f1-score, recall, and mean absolute error are the measures of success are studied as well as a comparison of the suggested approach with a few other existing methods

A Deep Learning Architecture For Histology Image Classification

Over the past decade, a machine learning technique called deep-learning has gained prominence in computer vision because of its ability to extract semantics from natural images. However, in contrast to the natural images, deep learning methods have been less effective for analyzing medical histology images. Analyzing histology images involves the classification of tissue according to cell types and states, where the differences in texture and structure are often subtle between states. These qualitative differences between histology and natural images make transfer learning difficult and limit the use of deep learning methods for histology image analysis. This dissertation introduces two novel deep learning architectures, that address these limitations. Both provide intermediate hints to aid deep learning models. The first deep learning architecture is constructed based on stacked autoencoders with an additional layer, called a hyperlayer. The hyperlayer is an intermediate hint that captures image features at different scales. The second architecture is a two-tiered Convolutional Neural Networks (CNN), with an intermediate representation, called a pixel/region labeling. The pixel/region labels provide a normalized semantic description that can be used as an input to a subsequent image classifier. The experiments show that by adding the hyperlayer, the architecture substantially outperforms fine-tuned CNN models trained without an intermediate target. In addition, the experiments suggest that the advantages of the labeling classifier are threefold. First, it generalizes to other related vision tasks. Second, image classification does not require extremely accurate pixel labeling. The architecture is robust and not susceptible to the noise. Lastly, labeling model captures low-level texture information and converts them to valuable hints.Doctor of Philosoph

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table