In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting human brain tumors in a magnetic resonance imaging (MRI) image. In this proposed algorithm, firstly, the template-based K-means algorithm is used to initialize segmentation significantly through the perfect selection of a template, based on gray-level intensity of image; secondly, the updated membership is determined by the distances from cluster centroid to cluster data points using the fuzzy C-means (FCM) algorithm while it contacts its best result, and finally, the improved FCM clustering algorithm is used for detecting tumor position by updating membership function that is obtained based on the different features of tumor image including Contrast, Energy, Dissimilarity, Homogeneity, Entropy, and Correlation. Simulation results show that the proposed algorithm achieves better detection of abnormal and normal tissues in the human brain under small detachment of gray-level intensity. In addition, this algorithm detects human brain tumors within a very short time—in seconds compared to minutes with other algorithms.

Alzheimer’s Disease (AD) is a significant cause of dementia worldwide, and its progression from mild to severe affects an individual’s ability to perform daily activities independently. The accurate and early diagnosis of AD is crucial for effective clinical intervention. However, interpreting AD from medical images can be challenging, even for experienced radiologists. Therefore, there is a need for an automatic diagnosis of AD, and researchers have investigated the potential of utilizing Artificial Intelligence (AI) techniques, particularly deep learning models, to address this challenge. This study proposes a framework that combines a Vision Transformer (ViT) and a Gated Recurrent Unit (GRU) to detect AD characteristics from Magnetic Resonance Imaging (MRI) images accurately and reliably. The ViT identifies crucial features from the input image, and the GRU establishes clear correlations between these features. The proposed model overcomes the class imbalance issue in the MRI image dataset and achieves superior accuracy and performance compared to existing methods. The model was trained on the Alzheimer’s MRI Preprocessed Dataset obtained from Kaggle, achieving notable accuracies of 99.53% for 4-class and 99.69% for binary classification. It also demonstrated a high accuracy of 99.26% for 3-class on the AD Neuroimaging Initiative (ADNI) Baseline Database. These results were validated through a thorough 10-fold cross-validation process. Furthermore, Explainable AI (XAI) techniques were incorporated to make the model interpretable and explainable. This allows clinicians to understand the model’s decision-making process and gain insights into the underlying factors driving the AD diagnosis.

Presented here are the results of an investigation conducted to determine the effectiveness of deep learning (DL)‐based systems utilizing the power of transfer learning for detecting breast cancer in histopathological images. It is shown that DL models that are not specifically developed for breast cancer detection can be trained using transfer learning to effectively detect breast cancer in histopathological images. The outcome of the analysis enables the selection of the best DL architecture for detecting cancer with high accuracy. This should facilitate pathologists to achieve early diagnoses of breast cancer and administer appropriate treatment to the patient. The experimental work here used the BreaKHis database consisting of 7909 histopathological pictures from 82 clinical breast cancer patients. The strategy presented for DL training uses various image processing techniques for extracting various feature patterns. This is followed by applying transfer learning techniques in the deep convolutional networks like ResNet, ResNeXt, SENet, Dual Path Net, DenseNet, NASNet, and Wide ResNet. Comparison with recent literature shows that ResNext‐50, ResNext‐101, DPN131, DenseNet‐169 and NASNet‐A provide an accuracy of 99.8%, 99.5%, 99.675%, 99.725%, and 99.4%, respectively, and outperform previous studies.