The dataset is maintained by VISION AND IMAGE PROCESSING LAB, University of Waterloo. The images of the dataset were extracted from the public databases DermIS and DermQuest, along with manual segmentations of the lesions.

The dataset was used in the following journal publication. [1] Glaister, J., A. Wong, and D. A. Clausi, "Automatic segmentation of skin lesions from dermatological photographs using a joint probabilistic texture distinctiveness approach", IEEE Transactions on Biomedical Engineering [2] Amelard, R., J. Glaister, A. Wong, and D. A. Clausi, "High-level intuitive features (HLIFs) for intuitive skin lesion descriptionpdf", IEEE Transactions on Biomedical Engineering, vol. 62, issue 3, pp. 820-831, October, 2015. [3] Glaister, J., R. Amelard, A. Wong, and D. A. Clausi, "MSIM: Multi-Stage Illumination Modeling of Dermatological Photographs for Illumination-Corrected Skin Lesion Analysis", IEEE Transactions on Biomedical Engineering, vol. 60, issue 7, pp. 1873 - 1883, November, 2013.

Explore the Melanoma Cancer Image Dataset with 13,900 meticulously curated images. Ideal for machine learning, dermatology, and medical education.

Skin Cancer Dataset

This dataset contains skin cancer images labeled as benign (class 0) or malignant (class 1). It can be used for various tasks related to skin cancer classification, such as image recognition, machine learning, and deep learning models.

  Class Labels

The dataset consists of two class labels:

Class 0: Benign Class 1: Malignant

  Number of Rows

The dataset contains 2,637 rows, each corresponding to a unique skin cancer image.

  Usage

To… See the full description on the dataset page: https://huggingface.co/datasets/Falah/skin-cancer.

Here are a few use cases for this project:

1. Healthcare Diagnostics: Dermatologists can use the "skin cancer classification" model to supplement their diagnosis process, applying it to images of skin abnormalities to determine if they could be indicative of specific types of skin cancer like nv, mel, bkl, etc.

2. Telemedicine Applications: The model can be integrated into telemedicine platforms allowing users to upload images of suspicious skin patches for preliminary screening and classification of potential skin cancer, enhancing patient triage before visiting a specialist.

3. Educational Tools: Medical schools and training programs can use the model as an educational tool to train upcoming dermatologists on skin cancer types, demonstrating real-world detection and classification.

4. Mobile Health Apps: Health app developers can utilize the model in skin health monitoring applications, enabling users to routinely check and monitor skin abnormalities. Depending on the model's output, users can be prompted to visit a medical professional further for examination.

5. Pharmaceutical Research: The model could be used in clinical trials or drug research to monitor the effectiveness of skin cancer treatments over time, by classifying and comparing the cancer type before and after the trial period.

Melanoma Skin Cancer Dataset contains 10000 images. Melanoma skin cancer is deadly cancer, early detection and cure can save many lives. This dataset will be useful for developing the deep learning models for accurate classification of melanoma. Dataset consists of 9600 images for training the model and 1000 images for evaluation of model.

This dataset contains key characteristics about the data described in the Data Descriptor A patient centric dataset of images and metadata for identifying melanomas using clinical context. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Description:

👉 Download the dataset here

Welcome to the Melanoma Cancer Image Dataset, a carefully curated collection of 13,900 high-quality images. This dataset is designed to support advancements in dermatology and computer-aided diagnostics. Each image is a valuable tool in the fight against melanoma, an aggressive form of skin cancer. With this dataset, researchers and practitioners can explore and develop new machine learning models that can improve early detection and diagnosis.

Download Dataset

This dataset contains 13,900 images, each sized at 224 x 224 pixels. These images offer a detailed and consistent view of various melanoma manifestations. They show the diverse characteristics of both benign and malignant skin lesions, making the dataset a comprehensive resource for developing and testing diagnostic algorithms.

Key Features

High-Quality Images: All images are carefully selected and curated to ensure clarity and consistency, facilitating accurate analysis.

Diverse Manifestations: The dataset includes a wide range of melanoma presentations, aiding in the creation of robust diagnostic models.

Uniform Size: Each image is standardized to 224 x 224 pixels, making it easier to integrate into various machine learning pipelines.

Context

Melanoma is one of the deadliest forms of skin cancer, so a swift and precise diagnosis is crucial for improving patient outcomes. However, traditional diagnostic methods often face challenges because of the subtle differences between benign and malignant lesions. Therefore, this dataset uses cutting-edge imaging technology to enable the development of advanced machine learning models. As a result, these models can accurately differentiate between these critical conditions.

Applications

Machine Learning Research: Ideal for training and testing algorithms in image classification, segmentation, and pattern recognition.

Dermatology: Supports the development of AI tools for early melanoma detection, potentially improving diagnostic accuracy and patient care.

Medical Education: A valuable resource for training healthcare professionals in recognizing and understanding melanoma.

This dataset is sourced from Kaggle.

** RD DATASET ** RD dataset was created by the images from the melanoma community on the internet (https://reddit.com/r/melanoma). Consecutive images were included using a python library (https://github.com/aliparlakci/bulk-downloader-for-reddit) from Jan 25, 2020, to July 30, 2021. The ground truth was voted by four dermatologists and one plastic surgeon while referring to the chief complaint and brief history. A total of 1,282 images (1,201 cases) were finally included. Because of the deleted cases by users, the links of 860 cases are valid in July 2021.

1. RD_RAW.xlsx The download links and ground truth of the RD dataset are included in this excel file. In addition, the raw data of the AI (Model Dermatology Build2021 - https://modelderm.com) and 32 laypersons were included.

2. v1_public.zip "v1_public.zip" includes the 1,282 lesional images (full-size). The 24 images that were excluded from the study are also available.

3. v1_private.zip is not available here. Wide field images are not available here. If the archive is needed for research purpose, please email to Dr. Han Seung Seog (whria78@gmail.com) or Dr Cristian Navarrete-Dechent (ctnavarr@gmail.com).

References - The Degradation of Performance of a State-of-the-art Skin Image Classifier When Applied to Patient-driven Internet Search - Scientific Report (in-press)

** Background normal test with the ISIC images ** ISIC dataset (https://www.isic-archive.com; Gallery -> 2018 JID Editorial images; 99 images; ISIC_0024262 and ISIC_0024261 are identical images and ISIC_0024262 was skipped) was used for the background normal test. We defined 10% area rectangle crop to “specialist-size crop”, and 5% area rectangle crop to “layperson-size crop” a) S-crops.zip: specialist-size crops Format: CROPNO_AGE(0~99)_GENDER(1=male,0=female)[m]_FILENAME.png b) L-crops.zip: layperson-size crops Format: CROPNO_AGE(0~99)_GENDER(1=male,0=female)[m]_FILENAME.png c) result_S.zip: Background normal test result using the specialist-size crops d) result_L.zip; Background normal test result using the layperson-size crops

Reference - Automated Dermatological Diagnosis: Hype or Reality? - https://doi.org/10.1016/j.jid.2018.04.040 - Multiclass Artificial Intelligence in Dermatology: Progress but Still Room for Improvement - https://doi.org/10.1016/j.jid.2020.06.040

as well as approximately 1

The goal of the challenge is to help participants develop image analysis tools to enable the automated diagnosis of melanoma from dermoscopic images. Image analysis of skin lesions is composed of 3 parts: - Part 1: Lesion Segmentation - Part 2: Detection and Localization of Visual Dermoscopic Features/Patterns - Part 3: Disease Classification This challenge provides training data (~2000 images) for participants to engage in all 3 components of lesion image analysis. A separate public validation dataset (~150 images) and blind held-out test dataset (~600 images) will be provided for participants to generate and submit automated results. ## Background ### Melanoma Skin cancer is a major public health problem, with over 5 million newly diagnosed cases in the United States each year. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths each year. ### Dermoscopy As pigmented lesions occurring on the surface of the skin, melanoma is amenable to early detection by

Skin Cancer Dataset Description

This dataset contains a comprehensive collection of dermoscopic skin lesion images curated for the detection and classification of various types of skin cancer. It is intended to support researchers, medical practitioners, and AI developers in building robust computer vision models for the automatic identification and diagnosis of skin cancer. The dataset includes 16,831 high-quality images, preprocessed and augmented for optimal training performance.

Each image is labeled with one of seven distinct skin lesion categories, which are:

AKIEC – Actinic Keratosis and Intraepithelial Carcinoma, BCC – Basal Cell Carcinoma, BKL – Benign Keratosis-like Lesions, DF – Dermatofibroma, MEL – Melanoma, NV – Melanocytic Nevi (moles), VASC – Vascular Lesions

The dataset is organized into three subsets: train, validation, and test, each containing folders corresponding to the seven lesion types.

To enhance model generalization, each image was preprocessed using the following techniques:

Auto-orientation (EXIF-stripping) Resized to 224x224 pixels (stretched to fit) Grayscale conversion (CRT phosphor)

Augmentation techniques were also applied to create varied samples:

Random cropping (0–20%) Random rotations (−40 to +40 degrees)

This dataset was sourced from the Roboflow platform, originally shared by a user from North South University, and exported under the CC BY 4.0 license. It serves as a valuable resource for developing deep learning-based classification systems to detect malignant and benign skin lesions using dermoscopic imaging.

Data Source:

https://universe.roboflow.com/north-south-university-9briv/skin-cancer-detection-wfldq

Provided by a Roboflow user

Lawhori/skin-cancer-dataset-with-images dataset hosted on Hugging Face and contributed by the HF Datasets community

Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using the feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.

Melanoma Skin Cancer

## Overview

Melanoma Skin Cancer is a dataset for object detection tasks - it contains Benign annotations for 200 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).

Explore the Skin Spots Classification Dataset featuring balanced images of benign and malignant skin lesions.

HAM10000 is a dataset of 10000 training images for detecting pigmented skin lesions. The authors collected dermatoscopic images from different populations, acquired and stored by different modalities.

Skin Cancer Segmentation

## Overview

Skin Cancer Segmentation is a dataset for object detection tasks - it contains Akiec Bcc Df Nv Vas Mel Bkl annotations for 2,226 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).

Dataset

This dataset was created by Naim Mhedhbi

Released under Data files © Original Authors

Contents

The increasing incidence of melanoma has recently promoted the development of computer-aided diagnosis systems for the classification of dermoscopic images. The PH² dataset has been developed for research and benchmarking purposes, in order to facilitate comparative studies on both segmentation and classification algorithms of dermoscopic images. PH² is a dermoscopic image database acquired at the Dermatology Service of Hospital Pedro Hispano, Matosinhos, Portugal.

Description

Welcome! This dataset, comprising 13,900 meticulously curated images, is a valuable resource for advancing the field of dermatology and computer-aided diagnostics. Dive into the intricate world of melanoma, where every pixel holds the potential to redefine early detection.

Context

Melanoma, a deadly form of skin cancer, demands prompt and accurate diagnosis. Leveraging state-of-the-art technology, this dataset empowers researchers and practitioners to develop robust machine-learning models capable of distinguishing between benign and malignant lesions. The images, uniformly sized at 224 x 224 pixels, offer a comprehensive view of melanoma's diverse manifestations.

Sources and Inspiration

This dataset draws inspiration from the critical need for advanced diagnostic tools in dermatology. The images are compiled from diverse sources and showcase the intricate features that challenge traditional diagnostic methods. By sharing this dataset on Kaggle, we invite the global data science community to collaborate, innovate, and contribute towards developing reliable models for melanoma classification.

How to Participate

1. Download the dataset and explore the wide variety of melanoma images.
2. Develop machine learning models for accurate classification.
3. Share your insights, challenges, and breakthroughs with the Kaggle community.
4. Together, let's push the boundaries of melanoma diagnosis and make a lasting impact on healthcare.

Join me in this exciting endeavor to enhance early detection, improve patient outcomes, and make strides in the fight against melanoma. The future of dermatology is in your hands!