An Automatic Dermatology Detection System Based on Deep Learning and Computer Vision

Abstract

Automatic medical diagnosis has gained significant attention among researchers, particularly in disease diagnosis. Differentiating between dermatology diseases is pivotal in clinical decision-making as it provides prognostic and predictive information and treatment strategies. This paper proposes a dermatology detection system based on deep learning (DL) and object recognition. The proposed model consists of three phases: Data preprocessing, data augmentation, and classification with localization. In the data preprocessing phase, we apply various operations such as color transformation, resizing, normalization, and labeling to prepare the input image for enrollment in our DL models. The data augmentation phase is carried out on the input images using the convolutional generative adversarial network algorithm. In the third phase, YOLO-V5 is used to classify and localize objects. The dataset is carefully collected with the assistance of medical specialists to ensure its accuracy. The proposed models are evaluated and compared using various metrics. Our empirical results demonstrate that the proposed model outperforms state-of-the-art models in terms of accuracy. Our proposed methodology offers significant improvements in detecting vitiligo and melanoma compared to recent techniques.

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

“…Equation ( 6) defines α as a trade-off parameter based on the expected box size (S) and IoU. Equation (7) introduces ν to assess aspect ratio coherence by comparing α and β. These equations are crucial for accurate bounding box evaluations and optimization in object detection.…”

“…Vision-based emergency vehicle detection (VEVD) has received limited attention in research thus far, with only a few studies [5][6][7] dedicated to this area. A review conducted in [8] focused on different object detection algorithms and highlighted the suitability of YOLO [5] for vision-based EVD due to its fast processing speed.…”

“…To provide a concise overview, object detection was summarized in the initial phase. The object detectors used in the vehicle detection stage of [6,10] involved the utilization of the ImageNet [12] and MS COCO [7] datasets for the training of the object detectors. The object detectors were trained on datasets that were not specifically designed for the EVD problem, incorporating an extensive array of object classes that may not be directly relevant.…”