Journal of Image Processing and Artificial Intelligence

Heart Attack Risk Factor Analysis and Prediction Using Retinal Eye Images, Digital Image Processing, and Deep Learning Algorithms

S. Uday Kumar — 2026-05-30

Myocardial infarction continues to rank as the world's single most lethal medical event, responsible for an estimated 17.9 million fatalities annually. Ironically, much of that burden is preventable, provided that susceptible individuals are identified before their first acute episode. The difficulty is that existing risk-scoring methods, however scientifically sound, demand fasting blood draws and lipid assays that large segments of the global population simply cannot obtain. Authors set out to design a screening tool that bypasses these logistical barriers entirely. The approach harnesses a medically underused fact: the inner surface of the eye, specifically its network of tiny blood vessels, undergoes measurable structural changes driven by the same pathological forces that damage the coronary arteries. A photograph of the retina, therefore, encodes cardiac risk information that can be read by a trained algorithm. Authors built a three-stage learning architecture for this task. In the first stage, an Inception-v3 convolutional network—initialized on a broad natural-image corpus and subsequently specialized on fundus photographs—extracts a compact numerical representation of each patient's retinal morphology. In the second stage, Fuzzy C-Means organizes patients into graded risk strata whose boundaries reflect the probabilistic, spectrum-like nature of cardiovascular disease rather than imposing arbitrary hard thresholds. In the third stage, a two-layer Long Short-Term Memory network interprets the combined representation and delivers a binary verdict: elevated or non-elevated cardiac risk. Fourteen structured physiological aisbiochemical variables from the UCI Cleveland Heart Disease collection are woven into the feature space alongside the image-derived descriptors, enriching the model with complementary clinical context. The trained system is wrapped in a Flask web application accessible by patients and clinicians through role-differentiated dashboards. Tested against a held-out partition of the Cleveland dataset, the architecture attained 91.3 % classification accuracy and an area under the receiver-operating curve of 0.94. These figures exceed those of logistic regression (78.4%, AUC 0.82), conventional deep networks (83.6%, AUC 0.88), and all prior published methods on this benchmark. A systematic component ablation confirms that retinal imaging supplies predictive information orthogonal to structured clinical data.

Comparative Study of Lightweight CNN Architectures and FaceNet-based Transfer Learning for Face Recognition in Smart Attendance Systems

Mirza Naseeha Begum — 2026-05-08

Automated face recognition has emerged as a critical enabler of touchless attendance management, addressing growing demands for hygienic, contact-free identification in academic and professional environments. This paper presents a systematic comparative study of three custom-trained Convolutional Neural Network architectures — CNN-7, CNN-9, and CNN-11 — and a transfer-learning pipeline based on FaceNet (InceptionResnetV1 / VGGFace2) paired with Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Softmax classifiers. Experiments are conducted across three dataset configurations: (i) a controlled custom dataset of 1,890 face images from 30 subjects (15 male, 15 female), captured at nine illumination levels (−40% to +40% in 10% increments) and seven rotation angles (-90° to +90° in 30° increments); (ii) the publicly available PINS Face Recognition dataset comprising 50 celebrity subjects and 9,721 in-the-wild images; and (iii) a combined 80-class benchmark of 11,610 images formed by merging both sources. Among the custom CNN models, CNN-11 — comprising five convolutional blocks with Batch-Normalization and two Dropout regularisation layers — achieves the highest test accuracy of 100% on the custom dataset, followed by CNN-9 at 99.63% (F1 = 0.9963) and CNN-7 at 89.63%. The FaceNet + SVM pipeline attains perfect classification (100% accuracy, F1 = 1.0) on the isolated custom dataset and 99.66% accuracy (F1 = 0.9967) on the PINS dataset. On the combined 80-class benchmark, FaceNet + Softmax achieves 99.89% accuracy, with a five-fold cross-validation mean of 99.72% ± 0.04%, confirming robust generalisation across both controlled and unconstrained imaging conditions. All models are evaluated using accuracy, misclassification rate, sensitivity, specificity, precision, F1-score, and ROC-AUC. The system additionally incorporates a Duplicate Face Filter (DFF) module, an adaptive confidence-threshold real-time inference pipeline, and a desktop-based attendance monitoring application.

Smart System Using Deep Learning for Detection of Paddy Crop Diseases and Automated Pesticide Spraying

Chaithra K — 2026-05-13

Paddy a staple food crop that supports more than half of the world’s population. However, several situations, including rice blast, brown spot, bacterial scab, and scald, which can lower yield and result in significant losses, have a significant impact on its civilisation. Prior and accurate discovery of these conditions is vital for proper crop operation. The requirement for an automated outcome is highlighted by the fact that traditional methods that rely on manual evaluation are frequently laborious, time-consuming, and prone to errors. This research describes an AI-based approach that uses computer vision and deep learning to automatically detect paddy crop issues and target germicide dispersal. The system utilizes a Convolutional Neural Network (CNN), a type of computer model designed to dissect images, to examine images of paddy leaves and identify symptoms of complaint. A camera continuously captures videotape of the crop field, and frames are taken at regular intervals for processing. These frames undergo a preprocessing method similar to resizing, normalization, and data augmentation to enhance the delicacy and Mitigate Possible spelling mistakes. The trained CNN classifies the images as either healthy or diseased. When an issue is detected, a signal is transmitted to a Jeer Pi (a small, affordable computer), which activates a DC diaphragm pump to spot pesticides directly onto the affected area of the factory. This targeted system reduces the use of chemicals and lowers environmental impact. Overall, the proposed system enhances discovery delicacy, reduces manual labor, and supports precision agriculture by enabling real-time monitoring and effective control operations in paddy cultivation.