Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Kidney cancer is a very dangerous and lethal cancerous disease caused by kidney tumors or by genetic renal disease, and very few patients survive because there is no method for early prediction of kidney cancer. Early prediction of kidney cancer helps doctors start proper therapy and treatment for the patients, preventing kidney tumors and renal transplantation. With the adaptation of artificial intelligence, automated tools empowered with different deep learning and machine learning algorithms can predict cancers. In this study, the proposed model used the Internet of Medical Things (IoMT)-based transfer learning technique with different deep learning algorithms to predict kidney cancer in its early stages, and for the patient’s data security, the proposed model incorporates blockchain technology-based private clouds and transfer-learning trained models. To predict kidney cancer, the proposed model used biopsies of cancerous kidneys consisting of three classes. The proposed model achieved the highest training accuracy and prediction accuracy of 99.8% and 99.20%, respectively, empowered with data augmentation and without augmentation, and the proposed model achieved 93.75% prediction accuracy during validation. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of kidney cancer.

Brain tumor classification is critical for therapeutic applications that benefit from computer-aided diagnostics. Misdiagnosing a brain tumor can significantly reduce a patient's chances of survival, as it may lead to ineffective treatments. This study proposes a novel approach for classifying brain tumors in MRI images using Transfer Learning (TL) with state-of-the-art deep learning models: AlexNet, MobileNetV2, and GoogleNet. Unlike previous studies that often focus on a single model, our work comprehensively compares these architectures, fine-tuned specifically for brain tumor classification. We utilize a publicly available dataset of 4,517 MRI scans, consisting of three prevalent types of brain tumors—glioma (1,129 images), meningioma (1,134 images), and pituitary tumors (1,138 images)—as well as 1,116 images of normal brains (no tumor). Our approach addresses key research gaps, including class imbalance, through data augmentation and model efficiency, leveraging lightweight architectures like MobileNetV2. The GoogleNet model achieves the highest classification accuracy of 99.2%, outperforming previous studies using the same dataset. This demonstrates the potential of our approach to assist physicians in making rapid and precise decisions, thereby improving patient outcomes. The results highlight the effectiveness of TL in medical diagnostics and its potential for real-world clinical deployment. This study advances the field of brain tumor classification and provides a robust framework for future research in medical image analysis.

For detecting and diagnosing a wide range of ophthalmological diseases, fundus images are used as a primary and basic tool. Early and accurate diagnosis of these ocular diseases can substantially improve the quality of treatment as well as important for preventing permanent vision loss. Changes in the anatomical structures like the optic disc, macula, blood vessels, and fovea show the presence of diseases like age-related macular degeneration, glaucoma, diabetic retinopathy, cataracts, myopia, and hypertension. In the proposed work, six different automated convolutional neural network architectures based on the Internet of Medical Things (IoMT) using transfer learning techniques were implemented for the classification of fundus images that can detect ocular diseases. These pre-trained neural networks were tuned by modifying the four layers before training them on the dataset. The proposed models incorporate blockchain technology-based private clouds for the security of the patient’s data. Transfer learning provides a promising framework with the combination of IoMT technologies and blockchain technology layers to enhance the diagnosing capabilities of Ocular disease. An ocular disease dataset was used, which was classified into four classes Cataract, Glaucoma, Myopia, and AMD. Class-wise Accuracy, Precision, Sensitivity, F1 Score, Specificity, and Misclassification Rate were computed with up to 96.88% training and 95.40% testing accuracy. The second part of this research is a comparative analysis of implemented models. The performance of AlexNet, GoogleNet, MobileNetV2, DarkNet-19, VGG-19, and DenseNet-201 compared in terms of accuracy. GoogleNet yielded unquestionably impressive results when compared to AlexNet and MobileNetV2.

Background: X-chromosomal short tandem repeats (X-STRs) are crucial in forensic applications, particularly in complex kinship cases, and play an important role in population genetics. However, there is limited data on X-STR variation in Pakistani populations, especially among ethnic groups like Kashmiri and Punjabi. Methodology: This study investigates the forensic and genetic properties of 12 X-STRs from the Investigator Argus X-12 Kit (QIAGEN, Hilden, Germany) in 125 families (75 Kashmiri, 50 Punjabi) from Azad Jammu and Kashmir and Punjab, Pakistan. Results: In both populations, a total of 222 alleles were identified across the 12 X-STR loci (Punjabi 171 alleles, Kashmiri 161 alleles), with allele frequencies ranging from 0.0056 to 0.3033. DXS10148 was the most polymorphic locus with 28 alleles, while DXS7132 was the least polymorphic with 9 alleles. Most loci were in linkage equilibrium, except for the DXS10135/DXS10148 pair in males, with no loci exhibiting significant linkage disequilibrium in females. The combined power of discrimination was 0.999 999 9977 for Kashmiri males, 0.999 999 999 999 9746 for Kashmiri females, and 0.999 999 999 999 9781 for Punjabi females. In Kashmiri males, 34, 31, 28, and 32 haplotypes were observed across the four linkage groups (LG1, LG2, LG3, and LG4), though these groups did not form stable haplotypes, as indicated by Linkage Equilibrium within and significant Linkage Disequilibrium between groups. Conclusions: Genetic structure analysis using Principal Component Analysis and STRUCTURE revealed distinct clustering patterns for the Kashmiri and Punjabi populations, indicating unique genetic backgrounds and ancestry influences, particularly distinguishing them from East Asian populations. This study provides a comprehensive analysis of X-STR variation in Punjabi and Kashmiri populations, offering valuable insights for forensic and population genetic studies.