iToBoS is exploring the use of EfficientNet models for pigmented skin lesion classification. This family of models, developed by Google researchers, are deep learning architectures that offer exceptional performance.
The EfficientNet architectures achieve a balance between efficiency and accuracy through their compound scaling technique. This technique uniformly scales the network’s depth, width, and resolution, optimizing model size and computational requirements. EfficientNets achieve remarkable accuracy with fewer parameters, making them a breakthrough in the field. In image classification, object recognition, and semantic segmentation, EfficientNet models have outperformed earlier state-of-the-art models while requiring less processing power. Because of their effectiveness, they can be deployed on edge devices with constrained processing resources and in real-time applications.
EfficientNet models have showed potential in enhancing the precision and effectiveness of automated skin cancer detection systems for skin lesion diagnosis. They can occasionally outperform human professionals in identifying benign from malignant skin lesions. Fast inference times are made possible by EfficientNets’ efficiency, which makes them perfect for real-time diagnosis. In the iToBoS project, we are leveraging the power of EfficientNet models to advance the diagnosis and classification of skin moles and lesions obtained by the iToBoS scanner. By utilizing various architectures of EfficientNet, we aim to enhance the accuracy and efficiency of automated skin cancer detection systems. The exceptional efficiency of EfficientNet models makes them well-suited for processing the vast amount of data generated by the iToBoS scanner, enabling us to analyze clinical and dermoscopic images with high sensitivity and reliability. By implementing EfficientNet architectures in the iToBoS project, we aim to provide dermatologists and healthcare professionals with a valuable tool that can aid in the early detection and diagnosis of skin cancer.
The integration of EfficientNet models into the iToBoS project brings the potential for improved patient care and outcomes. By harnessing the capabilities of deep learning and EfficientNet’s efficiency, we can expedite the diagnosis process, allowing for timely interventions and treatments. Overall, the iToBoS project’s use of EfficientNet models constitutes a significant development for dermatology field. We intend to revolutionize skin mole and lesion diagnostics by fusing cutting-edge deep learning architecture with cutting-edge scanning technology, ultimately resulting in improved patient outcomes, increased diagnostic accuracy, and increased efficiency in the identification of skin cancer.
References:
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ArXiv. /abs/1905.11946.