🩺 Dermatology Disease Classification Dataset 📌 Overview This dataset contains 262,874 images of various skin conditions, categorized into 34 different disease classes. It is designed for deep learning-based image classification and can be used to train models for automatic dermatology diagnosis.

🏥 Disease Categories (34 Classes) The dataset includes images of various skin conditions, including but not limited to: ✅ Acne & Rosacea ✅ Actinic Keratosis & Malignant Lesions ✅ Atopic Dermatitis ✅ Eczema ✅ Melanoma & Moles ✅ Psoriasis & Lichen Planus ✅ Fungal Infections (Ringworm, Athlete’s Foot, Nail Fungus) ✅ Herpes, HPV, & STDs ✅ Viral Infections (Chickenpox, Shingles, Warts, Molluscum) ✅ Bacterial Infections (Cellulitis, Impetigo) ✅ Lupus & Connective Tissue Diseases ✅ Pigmentation Disorders ✅ Systemic Diseases with Skin Manifestations

📌 Full category list is available in the dataset.

🛠️ Usage This dataset is ideal for: ✅ Training deep learning models for dermatology image classification ✅ Developing AI-powered medical diagnosis tools ✅ Testing transfer learning models (ResNet, ConvNeXt, EfficientNet, etc.) ✅ Skin disease severity detection & progression tracking

📖 References & Acknowledgments This dataset is a compilation of publicly available dermatology image datasets and medical sources. It is intended for research and educational purposes only.

⚠️ Disclaimer This dataset is not a substitute for professional medical advice. Any models trained on this dataset should be validated by dermatologists before real-world deployment.

Human Faces Dataset

Dataset comprises 1,200+ high-quality facial images of 400 people, capturing diverse skin tones (lighter to darker), various skin types, and multiple poses (frontal, left/right profiles). Designed for dermatology research, it provides detailed annotations of skin diseases, lesions, acne, rashes, and other dermatological conditions, enabling robust disease detection, classification, and diagnosis. It is ideal for training deep learning models, improving detection… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/facial-skin-condition-dataset.

The dataset contains rash images of 11 different disease states. Images of normal skin are also included in the dataset.

Skin Diseases Acne Actinic Carcinoma Atopic Dermatitis Bullous Disease Cellulitis Eczema Drug Eruptions Herpes HPV Light Diseases Lupus Melanoma Poison IVY Psoriasis Benign Tumors Systemic Disease Ringworm Urticarial Hives Vascular Tumors Vasculitis Viral Infections

This dataset combines images from the open-source Skin Diseases Dataset (Hossain, 2022), Dermnet Skin Diseases Database (DermNet, 2022), and Dermatology Atlas (2022) databases. It contains 38,760 images of 6 different skin diseases: infectious skin diseases, eczema, acne, pigment diseases, benign and malignant tumors.

The data set is randomly divided 0.80:0.10:0.10 train, test and validation.

Dataset Title: Skin Disease Image Classification (Over 8000 Images)

Description:
This dataset contains over 8,000 high-quality images of skin conditions, categorized into five distinct disease classes. It is designed for the development and evaluation of machine learning models aimed at skin disease classification and recognition. Each image is labeled according to one of the following skin conditions:

1. Atopic Dermatitis: A chronic skin condition that causes itchy, inflamed skin, often appearing in patches, most commonly on the face, hands, feet, or behind the knees.
2. Eczema: A group of medical conditions that result in inflammation, redness, and itching of the skin, with various forms including atopic eczema and contact dermatitis.
3. Seborrheic Keratoses and other Benign Tumors: Non-cancerous, often raised growths on the skin, typically appearing in middle-aged or elderly individuals. They are typically tan, brown, or black in color.
4. Tinea Ringworm, Candidiasis, and other Fungal Infections: A collection of skin conditions caused by fungal infections, such as ringworm, athlete's foot, and candidiasis, presenting as red, itchy, scaly patches on the skin.
5. Psoriasis, Lichen Planus, and Related Diseases: A group of chronic skin diseases that cause the rapid growth of skin cells, leading to scaly, itchy patches. Lichen planus is an inflammatory condition that causes flat-topped, purplish lesions on the skin.

This dataset provides a wide variety of images, each representing different severities and variations of these conditions, making it ideal for training deep learning models to identify and differentiate between common skin diseases.

Features:
- Images: High-resolution skin images from various sources - Classes: 5 disease categories - Size: More than 8,000 labeled images, suitable for model training, validation, and testing.

Usage:
This dataset is perfect for: - Building models for automatic skin disease detection. - Assisting in the diagnosis and classification of common skin conditions. - Educational and research purposes in dermatology and medical image processing.

Description:

This dataset contains augmented images of six different dermatological conditions. Each category includes 399 images, providing a balanced dataset ideal for training machine learning models, particularly in the field of medical image analysis.

Categories:

1. Acne: A common skin condition that occurs when hair follicles become clogged with oil and dead skin cells, leading to pimples, blackheads, or whiteheads.
2. Carcinoma: A type of skin cancer that begins in the basal or squamous cells. The images in this category may show various stages and forms of skin carcinoma.
3. Eczema: A condition that makes the skin red, inflamed, itchy, and sometimes results in blisters. The images depict different manifestations of eczema.
4. Keratosis: A skin condition characterized by rough, scaly patches on the skin caused by excessive growth of keratin. This category includes images of various types of keratosis, such as actinic keratosis.
5. Milia: Small, white, benign bumps that typically appear on the face, especially around the eyes and on the cheeks. The images show different instances of this condition.
6. Rosacea: A chronic skin condition that causes redness and visible blood vessels in your face. This category contains images depicting the typical characteristics of rosacea.

Dataset Details:

Total Images: 2,394 Images per Category: 399 Image Format: JPEG Image Size: Variable. Augmentation Techniques: The images have been augmented using techniques such as rotation, flipping, zooming, and brightness adjustment to enhance the diversity of the dataset and improve model generalization.

A high-quality Facial Skin Diseases Dataset containing 188 labeled images of acne, designed for computer vision, dermatology AI, and object detection model training.

Skin Condition

## Overview

Skin Condition is a dataset for object detection tasks - it contains Skin Condition Ai annotations for 9,576 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Ski Problem Detection(Acne, Wrinkle, Dark circle).

We collected data from public databases containing curated, inferred, literature-based information to create a database for connecting biomedical information. With curated disease genes, phenotypes, and phenotype genes as the direct molecular signatures of rare skin diseases, this work tries to link potential drugs to candidate rare skin disease targets with matched genes through disease-gene or disease-phenotype-gene relationships.RSDB contains 891 rare skin diseases, 28,077 genes, 9,732 phenotypes and 16,671 compounds with 16,411 disease-gene relationships, 15,793 disease-phenotype relationships, 12,184 disease-reference relationships, 638,151 gene-phenotype relationships, 17,636 gene-reference relationships and 61,282 references.Users can visit the RSDB homepage (https://rsdb.cmdm.tw) to explore the data for rare skin disease information.The RSDB is under the Attribution-NonCommercial-ShareAlike 4.0 international License(https://creativecommons.org/licenses/by-nc-sa/4.0/).

• Skin diseases encompass a broad spectrum of conditions affecting the largest organ of the human body, ranging from common dermatological issues to more severe and potentially life-threatening disorders. Skin cancers, a subset of skin diseases, specifically involve the abnormal and uncontrolled growth of skin cells, often triggered by exposure to ultraviolet (UV) radiation, genetic factors, or environmental influences. Skin cancers, including melanoma, basal cell carcinoma, and squamous cell carcinoma, pose a significant health concern globally due to their prevalence and potential for metastasis. On the other hand, non-cancerous skin diseases, such as eczema, psoriasis, and acne, impact millions, affecting quality of life and sometimes leading to complications if left untreated. Research in this field is vital for understanding the complexities of skin diseases and cancers, developing effective detection methods, advancing treatment options, and ultimately improving outcomes for individuals affected by these conditions.

• Early detection, accurate diagnosis, and targeted interventions are key elements in the ongoing efforts to mitigate the impact of skin diseases and cancers on public health.

• In recent times, computer vision has shown great promise in conducting the classification and identification tasks of this kind.

• Fifty seven distinct kinds of skin diseases and skin cancer are shown in this large dataset, which can be used to develop machine vision-based techniques.

• In this dataset, there are 978 (primary source 90, secondary source 888) original images of skin diseases and skin cancer. Then, in order to increase the number of data points, shifting, flipping, zooming, shearing, brightness enhancement, and rotation techniques are used to create a total of 630 augmented images from these original images (primary source).

Cat Skin Disease

## Overview

Cat Skin Disease is a dataset for classification tasks - it contains Cat Skin Disease annotations for 348 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

This dataset is curated for training and validating machine learning models in the domain of dermatology and skin disease classification. It contains high-resolution skin images categorized into multiple skin conditions.

The dataset is divided into:

• train/: Contains training images organized into folders by disease class.
• val/: Contains validation images structured similarly.

Each folder represents a specific skin disease, and there’s a filelist.txt in each split to reference image paths and labels if needed.

Disease classes included:

• Acne
• Actinic Keratosis
• Basal Cell Carcinoma
• Chickenpox
• Dermato Fibroma
• Dyshidrotic Eczema
• Melanoma
• Nail Fungus
• Nevus
• Normal Skin
• Pigmented Benign Keratosis
• Ringworm
• Seborrheic Keratosis
• Squamous Cell Carcinoma
• Vascular Lesion

This dataset is ideal for:

• Building deep learning models for dermatological diagnosis.
• Transfer learning using pre-trained CNNs (ResNet, EfficientNet, etc.).
• Research in medical image analysis and classification.
• Developing clinical decision support systems.

Discover the booming skin condition analyzer market! Learn about its $500 million valuation in 2025, projected 8% CAGR, key drivers, and leading companies like Callegari and SkinLabs. Explore market trends and regional insights in this comprehensive analysis.

Skin Defects Dataset

The dataset contains images of individuals with various skin conditions: acne, skin redness, and bags under the eyes. Each person is represented by 3 images showcasing their specific skin issue. The dataset encompasses diverse demographics, age, ethnicities, and genders.

  The dataset is created on the basis of Facial Skin Condition Dataset

Types of defects in the dataset: acne, skin redness & bags under the eyes

Acne photos: display different… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/dermatology-dataset-acne-redness-and-bags-under-the-eyes.

The rise in the prevalence of skin diseases increases the demand for accurate and efficient diagnostic tools. Various traditional diagnostic tools face a few major struggles such as time-consuming and prone to error. Therefore, this paper proposes a novel and reliable skin disease classification framework by integrating advanced preprocessing, feature extraction, and ensemble learning approaches to enhance diagnostic accuracy. The proposed method involves extensive data collection from the Skin disease image dataset, Skin Disease Dataset, and 33k skin disease dataset. Then the collected data are preprocessed by applying key approaches such as normalization, resizing, noise reduction, and grayscale conversion to improve the image quality. The Crayfish Optimization Algorithm with a Reverse Wheel Strategy is applied for an effective feature segmentation, where the most relevant features are segmented. Then the feature extraction is performed using the Gray Level Co-occurrence Matrix. For classification, the Meta Ensemble-based Random Cat Gradient Boost model is introduced by combining the merits of multiple classifiers to enhance prediction performance. The experimental findings demonstrate that the proposed model achieves excellent accuracy and precision of 99.78% and 98.51%, respectively, on the Skin Disease Image Dataset.

The displayed data on skin condition shows results of the Global Consumer Survey conducted in France in 2021. Some ** percent of respondents answered the question "Which of the following characteristics apply to your facial skin?" with "Normal".

An integumentary system disease that is located_in skin. url:http://en.wikipedia.org/wiki/Skin_disease

BackgroundDue to the lower reliability of laboratory tests, skin diseases are more suitable for diagnosis with AI models. There are limited AI dermatology diagnostic models combining images and text; few of these are for Asian populations, and few cover the most common types of diseases.MethodsLeveraging a dataset sourced from Asia comprising over 200,000 images and 220,000 medical records, we explored a deep learning-based system for Dual-channel images and extracted text for the diagnosis of skin diseases model DIET-AI to diagnose 31 skin diseases, which covers the majority of common skin diseases. From 1 September to 1 December 2021, we prospectively collected images from 6,043 cases and medical records from 15 hospitals in seven provinces in China. Then the performance of DIET-AI was compared with that of six doctors of different seniorities in the clinical dataset.ResultsThe average performance of DIET-AI in 31 diseases was not less than that of all the doctors of different seniorities. By comparing the area under the curve, sensitivity, and specificity, we demonstrate that the DIET-AI model is effective in clinical scenarios. In addition, medical records affect the performance of DIET-AI and physicians to varying degrees.ConclusionThis is the largest dermatological dataset for the Chinese demographic. For the first time, we built a Dual-channel image classification model on a non-cancer dermatitis dataset with both images and medical records and achieved comparable diagnostic performance to senior doctors about common skin diseases. It provides references for exploring the feasibility and performance evaluation of DIET-AI in clinical use afterward.