A conditional generative adversarial networks and Yolov5 Darknet -based skin lesion localization and classification using independent component analysis model

Abstract

Automated diagnosis systems significantly improve the early detection of skin cancer, thereby contributing to its successful treatment. Skin lesion segmentation represents a crucial yet challenging issue within the realm of computer-assisted dermoscopy image analysis. Numerous segmentation techniques reliant on convolutional neural networks falter in achieving precise lesion boundaries due to the diminishing spatial scale of feature maps as they traverse through network layers. Nonetheless, neural networks demand a substantial amount of data, which presents a notable obstacle in the medical field. More data, and possibly a pre-trained model tailored to medical images, are imperative. Researchers have resorted to extensive augmentation and preprocessing methods, as well as fine-tuning the network using pre-trained models on unrelated images, in order to address the challenge of lesion segmentation as the initial step in skin cancer analysis. This arises from the fact that datasets are limited in size and encompass a diverse array of images, varying in terms of lighting, color, scale, and markings. The proposed method addresses the issue of precisely locating and classifying skin lesions in dermatological images. The architecture of Conditional Generative Adversarial Networks (cGAN) enables the generation of high-resolution, contextually pertinent images, thereby enhancing the discriminative properties of skin lesions. YOLOv5 Darknet has an effective object detection mechanism that aids in precise lesion localization. Independent component analysis (ICA), a signal processing technique that identifies statistically separate components from diverse data sources, is employed to improve the model's robustness and interpretability further. This facilitates extracting latent features from the combined dermatological images and data collection, thereby enhancing the model's understanding of complex lesion characteristics. According to experimental results on an extensive dermatological data set, the proposed cGAN-YOLOv5 technique is effective. In pinpointing and classifying lesion locations, the hybrid model outperforms the competition. The synergistic integration of cGANs, YOLOv5 Darknet, and ICA demonstrates the effectiveness of multifaceted integration in enhancing medical image analysis. Using high-resolution feature blocks and a noise-suppressing attention method, cGAN-YOLOv5 is proposed. Therefore, the proposed strategy segments skin lesions effectively.