DeTraC: Transfer Learning of Class Decomposed Medical Images in Convolutional Neural Networks

Due to the high availability of large-scale annotated image datasets, paramount progress has been made in deep convolutional neural networks (CNNs) for image classification tasks. CNNs enable learning highly representative and hierarchical local image features directly from data. However, the availability of annotated data, especially in the medical imaging domain, remains the biggest challenge in the field. Transfer learning can provide a promising and effective solution by transferring knowledge from generic image recognition tasks to the medical image classification. However, due to irregularities in the dataset distribution, transfer learning usually fails to provide a robust solution. Class decomposition facilitates easier to learn class boundaries of a dataset, and consequently can deal with any irregularities in the data distribution. Motivated by this challenging problem, the paper presents Decompose, Transfer, and Compose (DeTraC) approach, a novel CNN architecture based on class decomposition to improve the performance of medical image classification using transfer learning and class decomposition approach. DeTraC enables learning at the subclass level that can be more separable with a prospect to faster convergence.We validated our proposed approach with three different cohorts of chest X-ray images, histological images of human colorectal cancer, and digital mammograms. We compared DeTraC with the state-of-the-art CNN models to demonstrate its high performance in terms of accuracy, sensitivity, and specificity

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases

XDecompo: eXplainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification

Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations

Fatigue behavior of carbon/epoxy AFP laminates containing gaps

Composite materials are widely used in many applications owing to their advantages over conventional ones. Among the different available manufacturing techniques of composites, automated fiber placement (AFP) attracted the attention of many industries due to its speed of material deposition and repeatability in manufacturing. Unfortunately, the occurrence of gaps between material strips during AFP manufacturing process is unavoidable. Even though there have been many studies that focused on the effect of these gaps on the static properties of the AFP laminates, to our knowledge, no work has been performed to test their effect on fatigue behavior. In this dissertation, the effect of the induced gaps on fatigue performance of carbon/epoxy AFP laminates was investigated both experimentally and numerically. In the experimental part, fatigue tests were conducted on both reference, free from defects laminates, and defective laminates. Then, the fatigue performance of both types was compared and analyzed to obtain the effect of gaps. For better understanding the fatigue behavior of laminates containing gaps, many parameters were taken into consideration such as laminate stacking sequence, gap shape, gap orientation and number of gaps. Based on analyzing the results of fatigue testing of different stacking sequences, a few design recommendations are provided that can enhance the performance of the defective laminates and alleviate the effect of gaps. In addition, infrared thermography was used as a non-destructive technique for in-situ detection of damage during fatigue loading. In order to examine the nature of the inherent damage within the laminate due to gaps, sectioning and inspection of specimens using scanning electron microscopy (SEM) were performed. The extensive fatigue experiments revealed the existence of a threshold stress value below which the effect of gaps on fatigue performance diminishes. The main drawbacks in obtaining this threshold values using the traditional long fatigue testing method were the large number of specimens and long-time for the fatigue tests. Consequently, infrared thermography and Risitano method were applied on AFP laminates containing gaps to provide a quick method for obtaining the threshold values. This method has a great potential in saving time and material required for performing traditional fatigue tests to develop stress/life curves. The obtained results of threshold values were in good agreement with the results obtained from the conventional method. In the numerical part, a fatigue progressive damage model (FPDM) was developed using Ansys Parametric Design Language (APDL) and applied to the case of laminates containing gaps. The progressive damage model presented in this work is an integration of fatigue life model, failure criterion, sudden and gradual degradation of strength/stiffness. The predicted results from the model were compared to the experimental results for different stacking sequences. The model showed a good agreement with the experimental results for the case of unidirectional laminates. For the case of cross-ply laminates more work should be done for better prediction of results due to the complex nature of damage for off-axis laminates. Nevertheless, the model can be helpful in saving time and material in the preliminary design steps to have an idea about the damage behavior and the performance of the designed part