Search Results (12)

Background/Objectives: Skin cancer is one of the most common diseases in the world, whose early and accurate detection can have a survival rate more than 90% while the chance of mortality is almost 80% in case of late diagnostics. Methods: A modified EfficientNet-B0 is developed based on mobile inverted bottleneck convolution with squeeze and excitation approach. The 3 × 3 convolutional layer is used to capture low-level visual features while the core features are extracted using a sequence of Mobile Inverted Bottleneck Convolution blocks having both 3 × 3 and 5 × 5 kernels. They not only balance fine-grained extraction with broader contextual representation but also increase the network’s learning capacity while maintaining computational cost. The proposed architecture hyperparameters and extracted feature vectors of standard benchmark datasets (HAM10000, ISIC 2019 and MSLD v2.0) of dermoscopic images are optimized with the quantum-behaved particle swarm optimization algorithm (QBPSO). The merit function is formulated by the training loss given in the form of standard classification cross-entropy with label smoothing, mean fitness value (mf^val), average accuracy (mAcc), mean computational time (mCT) and other standard performance indicators. Results: Comprehensive scenario-based simulations were performed using the proposed framework on a publicly available dataset and found an mAcc of 99.62% and 92.5%, mfval of 2.912 × 10⁻¹⁰ and 1.7921 × 10⁻⁸, mCT of 501.431 s and 752.421 s for HAM10000 and ISIC2019 datasets, respectively. The results are compared with state of the art, pre-trained existing models like EfficentNet-B4, RegNetY-320, ResNetXt-101, EfficentNetV2-M, VGG-16, Deep Lab V3 as well as reported techniques based on Mask RCCN, Deep Belief Net, Ensemble CNN, SCDNet and FixMatch-LS techniques having varying accuracies from 85% to 94.8%. The reliability of the proposed architecture and stability of QBPSO is examined through Monte Carlo simulation of 100 independent runs and their statistical soundings. Conclusions: The proposed framework reduces diagnostic errors and assists dermatologists in clinical decisions for an improved patient outcomes despite the challenges like data imbalance and interpretability. Full article

Accurate identification of wild edible macrofungi is essential for biodiversity conservation, food safety, and ecological sustainability, yet remains challenging due to the morphological similarity between edible and toxic species. In this study, a curated dataset of 24 wild edible macrofungi species was analyzed using six state-of-the-art convolutional neural networks (CNNs) and four ensemble configurations, benchmarked across eight evaluation metrics. Among individual models, EfficientNetB0 achieved the highest performance (95.55% accuracy), whereas MobileNetV3-L underperformed (90.55%). Pairwise ensembles yielded inconsistent improvements, highlighting the importance of architectural complementarity. Notably, the proposed Combination Model, integrating EfficientNetB0, ResNet50, and RegNetY through a hierarchical voting strategy, achieved the best results with 97.36% accuracy, 0.9996 AUC, and 0.9725 MCC, surpassing all other models. To enhance interpretability, explainable AI (XAI) methods Grad-CAM, Eigen-CAM, and LIME were employed, consistently revealing biologically meaningful regions and transforming the framework into a transparent decision-support tool. These findings establish a robust and scalable paradigm for fine-grained fungal classification, demonstrating that carefully engineered ensemble learning combined with XAI not only advances mycological research but also paves the way for broader applications in plant recognition, spore analysis, and large-scale vegetation monitoring from satellite imagery. Full article

As a well-known World Cultural Heritage Site, the Historic Centre of Macao’s historical buildings possess a wealth of decorative patterns. These patterns contain cultural esthetics, geographical environment, cultural traditions, and other elements from specific historical periods, deeply reflecting the evolution of religious rituals and political and economic systems throughout history. Through long-term research, this article constructs a dataset of 11,807 images of local decorative patterns of historical buildings in Macau, and proposes a fine-grained image classification method using the ConvNeXt-L model. The ConvNeXt-L model is an efficient convolutional neural network that has demonstrated excellent performance in image classification tasks in fields such as medicine and architecture. Its outstanding advantages lie in limited training samples, diverse image features, and complex scenes. The most typical advantage of this model is its structural integration of key design concepts from a Transformer, which significantly enhances the feature extraction and generalization ability of samples. In response to the objective reality that the decorative patterns of historical buildings in Macau have rich levels of detail and a limited number of functional building categories, ConvNeXt-L maximizes its ability to recognize and classify patterns while ensuring computational efficiency. This provides a more ideal technical path for the classification of small-sample complex images. This article constructs a deep learning system based on the PyTorch 1.11 framework and compares ResNet50, EfficientNet-B7, ViT-B/16, Swin-B, RegNet-Y-16GF, and ConvNeXt series models. The results indicate a positive correlation between model performance and structural complexity, with ConvNeXt-L being the most ideal in terms of accuracy in decorative pattern classification, due to its fusion of convolution and attention mechanisms. This study not only provides a multidimensional exploration for the protection and revitalization of Macao’s historical and cultural heritage and enriches theoretical support and practical foundations but also provides new research paths and methodological support for artificial intelligence technology to assist in the planning and decision-making of historical urban areas. Full article

The current study presents a multi-class, image-based classification of eight morphologically similar macroscopic Earthstar fungal species (Astraeus hygrometricus, Geastrum coronatum, G. elegans, G. fimbriatum, G. quadrifidum, G. rufescens, G. triplex, and Myriostoma coliforme) using deep learning and explainable artificial intelligence (XAI) techniques. For the first time in the literature, these species are evaluated together, providing a highly challenging dataset due to significant visual overlap. Eight different convolutional neural network (CNN) and transformer-based architectures were employed, including EfficientNetV2-M, DenseNet121, MaxViT-S, DeiT, RegNetY-8GF, MobileNetV3, EfficientNet-B3, and MnasNet. The accuracy scores of these models ranged from 86.16% to 96.23%, with EfficientNet-B3 achieving the best individual performance. To enhance interpretability, Grad-CAM and Score-CAM methods were utilised to visualise the rationale behind each classification decision. A key novelty of this study is the design of two hybrid ensemble models: EfficientNet-B3 + DeiT and DenseNet121 + MaxViT-S. These ensembles further improved classification stability, reaching 93.71% and 93.08% accuracy, respectively. Based on metric-based evaluation, the EfficientNet-B3 + DeiT model delivered the most balanced performance, with 93.83% precision, 93.72% recall, 93.73% F1-score, 99.10% specificity, a log loss of 0.2292, and an MCC of 0.9282. Moreover, this modeling approach holds potential for monitoring symbiotic fungal species in agricultural ecosystems and supporting sustainable production strategies. This research contributes to the literature by introducing a novel framework that simultaneously emphasises classification accuracy and model interpretability in fungal taxonomy. The proposed method successfully classified morphologically similar puffball species with high accuracy, while explainable AI techniques revealed biologically meaningful insights. All evaluation metrics were computed exclusively on a 10% independent test set that was entirely separate from the training and validation phases. Future work will focus on expanding the dataset with samples from diverse ecological regions and testing the method under field conditions. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

Background—Diabetes is a rapidly spreading chronic disease that poses a significant risk to individual health as the population grows. This increase is largely attributed to busy lifestyles, unhealthy eating habits, and a lack of awareness about the disease. Diabetes impacts the human body in various ways, one of the most serious being diabetic retinopathy (DR), which can result in severely reduced vision or even blindness if left untreated. Therefore, an effective early detection and diagnosis system is essential. As part of the Kingdom of Saudi Arabia’s Vision 2030 initiative, which emphasizes the importance of digital transformation in the healthcare sector, it is vital to equip healthcare professionals with effective tools for diagnosing DR. This not only ensures high-quality patient care but also results in cost savings and contributes to the kingdom’s economic growth, as the traditional process of diagnosing diabetic retinopathy can be both time-consuming and expensive. Methods—Artificial intelligence (AI), particularly deep learning, has played an important role in various areas of human life, especially in healthcare. This study leverages AI technology, specifically deep learning, to achieve two primary objectives: binary classification to determine whether a patient has DR, and multi-class classification to identify the stage of DR accurately and in a timely manner. The proposed model utilizes six pre-trained convolutional neural networks (CNNs): EfficientNetB3, EfficientNetV2B1, RegNetX008, RegNetX080, RegNetY006, and RegNetY008. In our study, we conducted two experiments. In the first experiment, we trained and evaluated different models using fundus images from the publicly available APTOS dataset. Results—The RegNetX080 model achieved 98.6% accuracy in binary classification, while the EfficientNetB3 model achieved 85.1% accuracy in multi-classification, respectively. For the second experiment, we trained the models using the APTOS dataset and evaluated them using fundus images from Al-Saif Medical Center in Saudi Arabia. In this experiment, EfficientNetB3 achieved 98.2% accuracy in binary classification and EfficientNetV2B1 achieved 84.4% accuracy in multi-classification, respectively. Conclusions—These results indicate the potential of AI technology for early and accurate detection and classification of DR. The study is a potential contribution towards improved healthcare and clinical decision support for an early detection of DR in Saudi Arabia. Full article

Traditional methods of agricultural disease detection rely primarily on manual observation, which is not only time-consuming and labor-intensive, but also prone to human error. The advent of deep learning has revolutionized plant disease detection by providing more accurate and efficient solutions. The management of potato diseases is critical to the agricultural industry, as these diseases can lead to substantial losses in crop production. The prompt identification and classification of potato leaf diseases are essential to mitigating such losses. In this paper, we present a novel approach that integrates a lightweight convolutional neural network architecture, RegNetY-400MF, with transfer learning techniques to accurately identify seven different types of potato leaf diseases. The proposed method not only enhances the precision of potato leaf disease detection but also reduces the computational and storage demands, with a mere 0.40 GFLOPs and a model size of 16.8 MB. This makes it well-suited for use on edge devices with limited resources, enabling real-time disease detection in agricultural environments. The experimental results demonstrated that the accuracy of the proposed method in identifying seven potato leaf diseases was 90.68%, providing a comprehensive solution for potato crop management. Full article

Tea is central to the culture and economy of the Middle East countries, especially in Iran. At some levels of society, it has become one of the main food items consumed by households. Bioactive compounds in tea, known for their antioxidant and anti-inflammatory properties, have proven to confer neuroprotective effects, potentially mitigating diseases such as Parkinson’s, Alzheimer’s, and depression. However, the popularity of black tea has also made it a target for fraud, including the mixing of genuine tea with foreign substitutes, expired batches, or lower quality leaves to boost profits. This paper presents a novel approach to identifying counterfeit Iranian black tea and quantifying adulteration with tea waste. We employed five deep learning classifiers—RegNetY, MobileNet V3, EfficientNet V2, ShuffleNet V2, and Swin V2T—to analyze tea samples categorized into four classes, ranging from pure tea to 100% waste. The classifiers, tested in both patched and non-patched formats, achieved high accuracy, with the patched MobileNet V3 model reaching an accuracy of 95% and the non-patched EfficientNet V2 model achieving 90.6%. These results demonstrate the potential of image processing and deep learning techniques in combating tea fraud and ensuring product integrity in the tea industry. Full article

The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection. Full article

Pomegranate is a temperature-sensitive fruit during postharvest storage. If exposed to cold temperatures above its freezing point for a long time, it will suffer from cold stress. Failure to pay attention to the symptoms that may occur during storage will result in significant damage. Identifying pomegranates susceptible to cold damage in a timely manner requires considerable skill, time and cost. Therefore, non-destructive and real-time methods offer great benefits for commercial producers. To this end, the purpose of this study is the non-destructive identification of healthy frozen pomegranates. First, healthy pomegranates were collected, and hyperspectral images were acquired using a hyperspectral camera. Then, to ensure that enough frozen pomegranates were collected for model training, all samples were kept in cold storage at 0 °C for two months. They were then transferred to the laboratory and hyperspectral images were taken from all of them again. The dataset consisted of frozen and healthy images of pomegranates in a ratio of 4:6. The data was divided into three categories, training, validation and test, each containing 1/3 of the data. Since there is a class imbalance in the training data, it was necessary to increase the data of the frozen class by the amount of its difference with the healthy class. Deep learning networks with ResNeXt, RegNetX, RegNetY, EfficientNetV2, VisionTransformer and SwinTransformer architectures were used for data analysis. The results showed that the accuracies of all models were above 99%. In addition, the accuracy values of RegNetX and EfficientNetV2 models are close to one, which means that the number of false positives is very small. In general, due to the higher accuracy of EfficientNetV2 model, as well as its relatively high precision and recall compared to other models, the F1 score of this model is also higher than the others with a value of 0.9995. Full article

Buckwheat plant height is an important indicator for producers. Due to the decline in agricultural labor, the automatic and real-time acquisition of crop growth information will become a prominent issue for farms in the future. To address this problem, we focused on stereo vision and a regression convolutional neural network (CNN) in order to estimate buckwheat plant height. MobileNet V3 Small, NasNet Mobile, RegNet Y002, EfficientNet V2 B0, MobileNet V3 Large, NasNet Large, RegNet Y008, and EfficientNet V2 L were modified into regression CNNs. Through a five-fold cross-validation of the modeling data, the modified RegNet Y008 was selected as the optimal estimation model. Based on the depth and contour information of buckwheat depth image, the mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and mean relative error (MRE) when estimating plant height were 0.56 cm, 0.73 cm, 0.54 cm, and 1.7%, respectively. The coefficient of determination (R2) value between the estimated and measured results was 0.9994. Combined with the LabVIEW software development platform, this method can estimate buckwheat accurately, quickly, and automatically. This work contributes to the automatic management of farms. Full article

Efficient skin cancer detection using images is a challenging task in the healthcare domain. In today’s medical practices, skin cancer detection is a time-consuming procedure that may lead to a patient’s death in later stages. The diagnosis of skin cancer at an earlier stage is crucial for the success rate of complete cure. The efficient detection of skin cancer is a challenging task. Therefore, the numbers of skilful dermatologists around the globe are not enough to deal with today’s healthcare. The huge difference between data from various healthcare sector classes leads to data imbalance problems. Due to data imbalance issues, deep learning models are often trained on one class more than others. This study proposes a novel deep learning-based skin cancer detector using an imbalanced dataset. Data augmentation was used to balance various skin cancer classes to overcome the data imbalance. The Skin Cancer MNIST: HAM10000 dataset was employed, which consists of seven classes of skin lesions. Deep learning models are widely used in disease diagnosis through images. Deep learning-based models (AlexNet, InceptionV3, and RegNetY-320) were employed to classify skin cancer. The proposed framework was also tuned with various combinations of hyperparameters. The results show that RegNetY-320 outperformed InceptionV3 and AlexNet in terms of the accuracy, F1-score, and receiver operating characteristic (ROC) curve both on the imbalanced and balanced datasets. The performance of the proposed framework was better than that of conventional methods. The accuracy, F1-score, and ROC curve value obtained with the proposed framework were 91%, 88.1%, and 0.95, which were significantly better than those of the state-of-the-art method, which achieved 85%, 69.3%, and 0.90, respectively. Our proposed framework may assist in disease identification, which could save lives, reduce unnecessary biopsies, and reduce costs for patients, dermatologists, and healthcare professionals. Full article