Diabetic Retinopathy (DR) is a major complication in human eyes among the diabetic patients. Early detection of the DR can save many patients from permanent blindness. Various artificial intelligent based systems have been proposed and they outperform human analysis in accurate detection of the DR. In most of the traditional deep learning models, the cross-entropy is used as a common loss function in a single stage end-to-end training method. However, it has been recently identified that this loss function has some limitations such as poor margin leading to false results, sensitive to noisy data and hyperparameter variations. To overcome these issues, supervised contrastive learning (SCL) has been introduced. In this study, SCL method, a two-stage training method with supervised contrastive loss function was proposed for the first time to the best of authors' knowledge to identify the DR and its severity stages from fundus images (FIs) using "APTOS 2019 Blindness Detection" dataset. "Messidor-2" dataset was also used to conduct experiments for further validating the model's performance. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied for enhancing the image quality and the pre-trained Xception CNN model was deployed as the encoder with transfer learning. To interpret the SCL of the model, t-SNE method was used to visualize the embedding space (unit hyper sphere) composed of 128 D space into a 2 D space. The proposed model achieved a test accuracy of 98.36%, and AUC score of 98.50% to identify the DR (Binary classification) and a test accuracy of 84.364%, and AUC score of 93.819% for five stages grading with the APTOS 2019 dataset. Other evaluation metrics (precision, recall, F1-score) were also determined with APTOS 2019 as well as with Messidor-2 for analyzing the performance of the proposed model. It was also concluded that the proposed method achieved better performance in detecting the DR compared to the conventional CNN without SCL and other state-of-the-art methods.

Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.

This paper exploits the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes. DR, a leading cause of blindness worldwide, is a sort of swelling leakage due to excessive blood sugar in the retina vessels. An early-stage diagnosis is therefore beneficial to prevent diabetes patients from losing their sight. This study introduced a novel method to detect DR for binary class and multiclass classification based on the APTOS-2019 blindness detection and Messidor-2 datasets. First, DR images have been pre-processed using Ben Graham’s approach. After that, contrast limited adaptive histogram equalization (CLAHE) has been used to get contrast-enhanced images with lower noise and more distinguishing features. Then a novel hybrid convolutional neural network-singular value decomposition model has been developed to reduce input features for classifiers. Finally, the proposed method uses an ELM algorithm as the classifier that minimizes the training time cost. The experiments focus on accuracy, precision, recall, and F1-score and demonstrate the feasibility of adopting the proposed scheme for DR diagnosis. The method outperforms the existing techniques and shows an optimistic accuracy and recall of 99.73% and 100%, respectively, for binary class. For five stages of DR classification, the proposed model achieved an accuracy of 98.09% and 96.26% for APTOS-2019 and Messidor-2 datasets, respectively, which outperformed the existing state-of-art models.

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.

In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite difficult and time-consuming to diagnose due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgment. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network - principle component analysis (CNN-PCA) based feature extraction (ECP), and ECP with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.