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The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis. [ABSTRACT FROM AUTHOR]

Monitoring infrastructures is becoming an important and challenging issue. In Italy, the heritage consists of more than 60,000 bridges, which need to be inspected and detected in order to guarantee their strength and durability function during nominal lifespan. In this paper, a non-destructive survey methodology for study concrete bridges surface deterioration and viaducts is presented. Terrestrial and unmanned aerial vehicle (UAV) photogrammetry has been used for visual inspection of a standard concrete overpass in L'Aquila (Italy). The obtained orthomosaic has been processed by means of Object-Based Image Analysis (OBIA) to identify and classify deteriorated areas and decay forms. The results show a satisfactory identification and survey of deteriorated areas. It has also been possible to quantify metric information, such as width and length of cracks and extension of weathered areas. This allows to perform easy and fast periodic inspections over time in order to evaluate the evolution of deterioration and plan urgency of preservation or maintenance measures. [ABSTRACT FROM AUTHOR]