The implementation of AI assisted cancer detection systems in clinical environments has faced numerous hurdles, mainly because of the restricted explainability of their elemental mechanisms, even though such detection systems have proven to be highly effective. Medical practitioners are skeptical about adopting AI assisted diagnoses as due to the latter’s inability to be transparent about decision making processes. In this respect, explainable artificial intelligence (XAI) has emerged to provide explanations for model predictions, thereby overcoming the computational black box problem associated with AI systems. In this particular research, the focal point has been the exploration of the Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) approaches which enable model prediction explanations. This study used an ensemble model consisting of three convolutional neural networks(CNN): InceptionV3, InceptionResNetV2 and VGG16, which was based on averaging techniques and by combining their respective predictions. These models were trained on the Kvasir dataset, which consists of pathological findings related to gastrointestinal cancer. An accuracy of 96.89% and F1-scores of 96.877% were attained by our ensemble model. Following the training of the ensemble model, we employed SHAP and LIME to analyze images from the three classes, aiming to provide explanations regarding the deterministic features influencing the model’s predictions. The results obtained from this analysis demonstrated a positive and encouraging advancement in the exploration of XAI approaches, specifically in the context of gastrointestinal cancer detection within the healthcare domain.

Due to the progress in image processing and Artificial Intelligence (AI), it is now possible to develop automated tool for the early detection and diagnosis of Alzheimer’s Disease (AD). Handcrafted techniques developed so far, lack generality, leading to the development of deep learning (DL) techniques, which can extract more relevant features. To cater for the limited labelled datasets and requirement in terms of high computational power, transfer learning models can be adopted as a baseline. In recent years, considerable research efforts have been devoted to developing machine learning-based techniques for AD detection and classification using medical imaging data. This survey paper comprehensively reviews the existing literature on various methodologies and approaches employed for AD detection and classification, with a focus on neuroimaging techniques such as structural MRI, PET, and fMRI. The main objective of this survey is to analyse the different transfer learning models that can be used for the deployment of deep convolution neural network for AD detection and classification. The phases involved in the development namely image capture, pre-processing, feature extraction and selection are also discussed in the view of shedding light on the different phases and challenges that need to be addressed. The research perspectives may provide research directions on the development of automated applications for AD detection and classification.

Over the years, machine learning and deep learning have shown tremendous potential and good accuracy in the detection of Chronic Kidney Disease (CKD). Yet, these models are being criticised for their opaque decision-making process. To date, few studies have been conducted to look into which variables are more relevant in determining CKD. In this study, we first used two (2) ensemble approaches: XGBoost, AdaBoost. for detecting CKD. Next, we propose a model that uses deep neural networks (DNN) to extract and represent the features for the prognosis of CKD. The DNN was enhanced by fine-tuning the hyperparameters and it outperformed the ensemble methods with an accuracy of 98.8%. Finally, SHapley Additive exPlanations (SHAP), which is a technique that has attracted attention in the field of explainable artificial intelligence (XAI), was used to investigate which features contribute to predicting CKD and the results confirmed the medical explanations.

Esophageal cancer (EC) remains the disease that has the highest incidence and highest mortality rate in global cancer statistics, emphasising the imperative to enhance diagnostic precision and reliability through the use of advancing technologies. While AI-enhanced systems can improve the early detection of EC considerably, the prevalence of artefacts (1 in 4 frames) during endoscopy procedures compromises the developed systems significantly, leading to unreliable medical decision making. Vision transformer (ViT) networks, initially designed for natural language processing tasks, have demonstrated outstanding performance in handling medical images by presenting distinctive features advantageous for image processing. The application of ViT for detecting and classifying artefacts in endoscopic images, particularly in classifying colour misalignment artefacts is still subject to continual refinement and enhancement. This work aims to investigate the implementation of ViT for classification of colour misalignment artefacts in esophagus endoscopy images. Moreover, even though ViT has been a major breakthrough, its acceptance for real world applications is often jeopardised due to the lack of interpretability of how the classification results have been reached. Consequently, Explainable Artificial Intelligence (XAI) techniques have been explored to understand the criteria used to achieve the outcome. Several variants of the ViT and Data Efficient image Transformer (DeiT) networks have been fine-tuned and applied to our dataset in order to improve and evaluate their performance in colour misalignment classification in esophagus endoscopic images. Furthermore, XAI methods have been implemented to provide the criteria used by the network in reaching the classification results. Our fine-tuned ViT model, achieves an accuracy of 93.46%, precision of 93.48%, recall of 93.46% and F1 score of 93.46% surpassing InceptionResNetV2, a state-of-the-art model based on CNN, with an accuracy of 89.10%, precision of 89.10%, recall of 89.10% and F1 score of 88.23%. Additionally, the GradCAM XAI technique has been found to highlight the deterministic features used by the ViT model better than other XAI methods applied in this work. ViT achieves remarkable performance in classification of colour misalignment artefact outperforming CNNs, attributed to ViT’s enhanced ability to capture pixel relationships through self-attention weights. In addition, the intrinsic self-attention technique provides novel insights into the model’s decision-making mechanism.

Convolutional Neural Networks (CNNs) have been the state-of-the-art techniques applied in the field of medical imaging for numerous image processing tasks. Recently, vision transformer networks are emerging as another technique, complementing current CNNs in the medical field providing on-par performance while also having a number of unique characteristics that may be useful for medical image processing. While CNNs have been predominantly applied to artefact detection and classification in endoscopic images, ViT has been sparsely applied in this area. Additionally, both CNN and ViT have been sparingly applied to colour misalignment artefact classification. In this work, we, therefore, explore the application of Vision Transformer (ViT) in the classification of artefacts in endoscopic images of the gastrointestinal tract organs. Furthermore, the performance of ViT is compared to that of CNN in the classification of colour misalignment artefacts. Our customised ViT model, based on DeiT (Data-efficient image Transformers), has obtained an accuracy of 96.33% as compared to the CNN based Inceptionv3 model with an accuracy of 78.67% and InceptionResNetv2 with 76.67%. The results demonstrate that when pretrained on ImageNet, ViT offer better performance than CNNs in colour misalignment artefact classification. This is due to the ability of ViT to better depict the relationship between image pixels through self-attention weights. Moreover, the built-in self-attention mechanism offers fresh insight into the decision-making processes of the model.