Derm Pro AI
Introduction
Dataset and Training
Dataset and Training
As dermatologists dedicated to advancing patient care, we understand the critical importance of early melanoma detection. Derm Pro AI is our answer to this challenge, leveraging state-of-the-art deep learning technology to accurately detect melanoma from dermoscopic images.
Dataset and Training
Dataset and Training
Dataset and Training
Our model is trained on a comprehensive dataset comprising 33,126 unique images sourced from patients of diverse demographics. This rich dataset ensures exposure to various skin types, melanoma stages, and patient characteristics, enabling our model to perform with exceptional accuracy.
Model Architecture
Dataset and Training
Efficacy of AI in Melanoma Detection
At the core of DermPro AI lies a deep convolutional neural network (DCNN), meticulously trained to differentiate between melanoma and non-melanoma skin conditions. Unlike traditional models, Derm Pro AI focuses on a binary outcome—determining the presence or absence of melanoma with a confidence score ranging from 0% to 100%.
Efficacy of AI in Melanoma Detection
Efficacy of AI in Melanoma Detection
Efficacy of AI in Melanoma Detection
Scientific validation underscores the efficacy of AI in melanoma detection. Our model's exceptional performance in a rigorous evaluation, achieving a score of 0.9061, demonstrates its high reliability and effectiveness, positioning us competitively close to the top performers in the field.
Advanced Analysis
Efficacy of AI in Melanoma Detection
Competition Performance
Derm Pro AI conducts pixel-level analysis, meticulously examining each pixel within an image to identify patterns and features characteristic of melanoma. Additionally, our correlation analysis reveals valuable insights into the relationship between melanoma incidence, patient age, and gender, empowering dermatologists to make informed decisions.
Competition Performance
Efficacy of AI in Melanoma Detection
Competition Performance
Our exceptional achievement in the SIIM-ISIC Melanoma Classification challenge underscores the robustness and competitiveness of Derm Pro AI. Scoring 0.9061, we proudly lead the forefront of melanoma detection technology.
The study is not only significant in terms of AUC, SEN, and SPE for binary melanoma classification. The research results also show that customizing batch logic, loss function, and reforming fully connected layers are very important features for a high-performing model to classify medical images.
Join Us in Shaping the Future of Dermatological Care
Join Us in Shaping the Future of Dermatological Care
Join Us in Shaping the Future of Dermatological Care
"Derm Pro AI" represents a significant advancement in melanoma detection, offering dermatologists a valuable tool to aid in early diagnosis. With its exceptional accuracy, comprehensive training, and scientific validation, Derm Pro AI stands as a beacon of hope in the fight against melanoma. Join us in embracing the future of dermatological care with Derm Pro AI from HighVich Technologies.
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Join Us in Shaping the Future of Dermatological Care
Join Us in Shaping the Future of Dermatological Care
"introducing our innovative iOS app designed to revolutionize the field of dermatology – a cutting-edge tool that empowers dermatologists to efficiently assess and diagnose skin conditions with unprecedented accuracy. By seamlessly integrating artificial intelligence (AI) and machine learning (ML) technologies, our app provides a streamlined solution for dermatologists to analyze skin abnormalities directly through dermoscopic photos.
Studies and Articles
"Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review" - This systematic review, published by the NIH, explores various AI-based approaches for non-invasive melanoma detection.
"Artificial Intelligence in Dermatology: Where We Are and the Way to Go" - This article discusses the current state of AI in dermatology and its potential future applications.
"Applications of Artificial Intelligence in Dermatology" - This review article provides an overview of the applications of AI in dermatology, including skin cancer detection and diagnosis.
"Machine Learning and Artificial Intelligence in Dermatology: A Contemporary Overview" - This article provides an overview of machine learning and AI techniques used in dermatology, including skin cancer detection.
"Artificial Intelligence for the Diagnosis of Skin Cancer" - This review article discusses the role of AI in the diagnosis of skin cancer and its potential impact on clinical practice.