DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research

Shanawaj S Madarkar; Mahajabeen Madarkar; Madhumitha Venkatesh; Deepanshu Bansal; Teli Prakash; Konda Reddy Mopuri; Vinaykumar MV; KVL Sathwika; Adarsh Kasturi; Gandla Dilip Raj; PVN Supranitha; Harsh Udai

arXiv · 2506.06099

DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research

Shanawaj S Madarkar·Mahajabeen Madarkar·Madhumitha Venkatesh·Deepanshu Bansal·Teli Prakash·Konda Reddy Mopuri·Vinaykumar MV·KVL Sathwika·Adarsh Kasturi·Gandla Dilip Raj·PVN Supranitha·Harsh Udai

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Abstract

Artificial intelligence is poised to augment dermatological care by enabling scalable image-based diagnostics. Yet, the development of robust and equitable models remains hindered by datasets that fail to capture the clinical and demographic complexity of real-world practice. This complexity stems from region-specific disease distributions, wide variation in skin tones, and the underrepresentation of outpatient scenarios from non-Western populations. We introduce DermaCon-IN, a prospectively curated dermatology dataset comprising 5,450 clinical images from 3,002 patients across outpatient clinics in South India. Each image is annotated by board-certified dermatologists with 245 distinct diagnoses, structured under a hierarchical, aetiology-based taxonomy adapted from Rook's classification. The dataset captures a wide spectrum of dermatologic conditions and tonal variation commonly seen in Indian outpatient care. We benchmark a range of architectures, including convolutional models (ResNet, DenseNet, EfficientNet), transformer-based models (ViT, MaxViT, Swin), and Concept Bottleneck Models to establish baseline performance and explore how anatomical and concept-level cues may be integrated. These results are intended to guide future efforts toward interpretable and clinically realistic models. DermaCon-IN provides a scalable and representative foundation for advancing dermatology AI.

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Shanawaj S Madarkar, Mahajabeen Madarkar, Madhumitha Venkatesh, Deepanshu Bansal, Teli Prakash, Konda Reddy Mopuri, Vinaykumar MV, KVL Sathwika, Adarsh Kasturi, Gandla Dilip Raj, PVN Supranitha, Harsh Udai. 2026-01-08. DermaCon-IN: A Multi-concept Annotated Dermatological Image Dataset of Indian Skin Disorders for Clinical AI Research. https://arxiv.org/abs/2506.06099

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