A comprehensive analysis of eye diseases and medical data classification

Acute Lymphoblastic Leukemia (ALL) is a malignant neoplasm defined by the rapid proliferation of early lymphoid progenitors (lymphoblasts) within the bone marrow and peripheral blood. Due to its aggressive course, prompt and accurate diagnosis is essential and has a profound impact on patient outcomes. This study proposes an integrative deep learning method for ALL detection using the Acute Lymphoblastic Leukemia Image Database (ALL-IDB). This is accomplished by fusing one modified clinical data CNN integrated through an attention mechanism with another modified pre-trained CNN for image analysis. The performance of the proposed model was evaluated using the ALL-IDB1 and ALL-IDB2 datasets, achieving 99.2% accuracy with AUC at 0.998%. By incorporating clinical with image data, an overall increase of 2.3% in accuracy and 0.007 in AUC was observed. The results show that using deep learning to detect ALL is accurate and possible, laying the foundations for AI-based diagnoses of hematological cancers to be more accurate.

The prevalence of neck pain, a chronic musculoskeletal disease, has significantly increased due to the uncontrollable use of social media (SM) devices. The use of SM devices by younger generations increased enormously during the COVID-19 pandemic, being—in some cases—the only possibility for maintaining interpersonal, social, and friendship relationships. This study aimed to predict the occurrence of neck pain and its correlation with the intensive use of SM devices. It is based on nine quantitative parameters extracted from the retrospective X-ray images. The three parameters related to angle_1 (i.e., the angle between the global horizontal and the vector pointing from C7 vertebra to the occipito-cervical joint), angle_2 (i.e., the angle between the global horizontal and the vector pointing from C1 vertebra to the occipito-cervical joint), and the area between them were measured from the shape of the neck vertebrae, while the rest of the parameters were extracted from the images using the gray-level co-occurrence matrix (GLCM). In addition, the users’ ages and the duration of the SM usage (H.mean) were also considered. The decision tree (DT) machine-learning algorithm was employed to predict the abnormal cases (painful subjects) against the normal ones (no pain). The results showed that angle_1, area, and the image contrast significantly increased statistically with the time of SM-device usage, precisely in the range of 2 to 9 h. The DT showed a promising result demonstrated by classification accuracy and F1-scores of 94% and 0.95, respectively. Our findings confirmed that the objectively detected parameters, which elucidate the negative impacts of SM-device usage on neck pain, can be predicted by DT machine learning.