Ensemble learning is a process that belongs to the artificial intelligence (AI) field. It helps to choose a robust machine learning (ML) model, usually used for data classification. AI has a large connection with image processing and feature classification, and it can also be successfully applied to analyzing fundus eye images. Diabetic retinopathy (DR) is a disease that can cause vision loss and blindness, which, from an imaging point of view, can be shown when screening the eyes. Image processing tools can analyze and extract the features from fundus eye images, and these corroborate with ML classifiers that can perform their classification among different disease classes. The outcomes integrated into automated diagnostic systems can be a real success for physicians and patients. In this study, in the form image processing area, the manipulation of the contrast with the gamma correction parameter was applied because DR affects the blood vessels, and the structure of the eyes becomes disorderly. Therefore, the analysis of the texture with two types of entropies was necessary. Shannon and fuzzy entropies and contrast manipulation led to ten original features used in the classification process. The machine learning library PyCaret performs complex tasks, and the empirical process shows that of the fifteen classifiers, the gradient boosting classifier (GBC) provides the best results. Indeed, the proposed model can classify the DR degrees as normal or severe, achieving an accuracy of 0.929, an F1 score of 0.902, and an area under the curve (AUC) of 0.941. The validation of the selected model with a bootstrap statistical technique was performed. The novelty of the study consists of the extraction of features from preprocessed fundus eye images, their classification, and the manipulation of the contrast in a controlled way.

This paper presents a hybrid study of convolutional neural networks (CNNs), machine learning (ML), and transfer learning (TL) in the context of brain magnetic resonance imaging (MRI). The anatomy of the brain is very complex; inside the skull, a brain tumour can form in any part. With MRI technology, cross-sectional images are generated, and radiologists can detect the abnormalities. When the size of the tumour is very small, it is undetectable to the human visual system, necessitating alternative analysis using AI tools. As is widely known, CNNs explore the structure of an image and provide features on the SoftMax fully connected (SFC) layer, and the classification of the items that belong to the input classes is established. Two comparison studies for the classification of meningioma tumours and healthy brains are presented in this paper: (i) classifying MRI images using an original CNN and two pre-trained CNNs, DenseNet169 and EfficientNetV2B0; (ii) determining which CNN and ML combination yields the most accurate classification when SoftMax is replaced with three ML models; in this context, Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were proposed. In a binary classification of tumours and healthy brains, the EfficientNetB0-SVM combination shows an accuracy of 99.5% on the test dataset. A generalisation of the results was performed, and overfitting was prevented by using the bagging ensemble method.

Peritumoral areas or microenvironments surrounding melanoma are unexplored and partially understood, so that, in the following paper, an algorithm that predicts the trend of the melanoma progression is proposed. Additionally, in case of melanoma the peritumoral area is significantly correlated with the texture that belongs inside the skin lesion. The proposed algorithm analyses the region of interest (ROI) with Normalized 2-D cross-correlation (NCC) method and predicts the pattern in the peritumoral area which is most similar with the texture of the melanoma. An important step is the detection of the peritumoral area, in which case, the mathematical morphology techniques were proposed. The verifying of similarity between the samples cropped from inside the melanoma and peritumoral area with Structural Similarity Index (SSIM) was performed. The main advantage of the proposed algorithm is that it can be applied on different medical image types and tumors. The algorithm was tested on two datasets 7-Point and PH2, and two computes.

Rapid and accurate diagnosis is crucial for the early detection of malaria caused by Plasmodium parasites. This study primarily focuses on identifying a suitable deep learning model for classifying malaria parasites. In an ablation process, we started with single-branch Convolutional Neural Network (SB-CNN) and dual-branch CNN (DB-CNN) architectures enhanced with attention mechanisms to improve feature representation. Specifically, we incorporate the Convolutional Block Attention Module (CBAM) to focus on both channel-wise and spatially important features. We also use the Efficient Channel Attention (ECA) module to capture local inter-channel relationships, while the Squeeze-and-Excitation (SE) block emphasizes globally significant feature maps, further improving the model’s ability to distinguish between classes. To identify the most effective model, we experimented with EfficientNet-B0 and EfficientNet-B3 as backbone networks and conducted an ablation study by integrating various attention modules, resulting in three DB-CNN variants. We applied t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the high-dimensional feature space between infected and uninfected samples. Utilizing the 5-fold cross-validation method on the Thick dataset, the best-performing models included architectures such as SB EfficientNet-B3, SB EfficientNet-B3 combined with CBAM, and DB EfficientNet-B3 across both branches, with CBAM in the first and SE in the second.