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https://github.com/tanujdargan/h4b-z3r0-project
Skin Cancer Analysis WebApp Hosted on Streamlit for Hack4Bengal Hackathon MLH Member Event
https://github.com/tanujdargan/h4b-z3r0-project
Last synced: about 2 months ago
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Skin Cancer Analysis WebApp Hosted on Streamlit for Hack4Bengal Hackathon MLH Member Event
- Host: GitHub
- URL: https://github.com/tanujdargan/h4b-z3r0-project
- Owner: tanujdargan
- Created: 2022-04-09T05:01:31.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-04-10T16:28:34.000Z (almost 3 years ago)
- Last Synced: 2023-12-07T17:28:18.193Z (about 1 year ago)
- Language: JavaScript
- Homepage: https://share.streamlit.io/tanujdargan/h4b-z3r0-project/main/app.py
- Size: 66.4 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
#
Derma Check## Link to the hosted app:
https://share.streamlit.io/tanujdargan/h4b-z3r0-project/main/app.py## Video Link:
https://www.youtube.com/watch?v=wEIngfK8uEE### Who are we?
We are a team of 4 members studying in the IB Diploma Program in JBCN International School, Oshiwara. We are enthusiastic and hard-working learners who love to code and come up with modern solutions to problems that are faced by the people in our society. Our passion for anything related to computer science and technology is never-ending and pushes us to do better as we go ahead.### Background On Our Project
Skin cancer is the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions.### Dataset
Link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2FDBW86TFrom the Harvard Website:
Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available dataset of dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human Against Machine with 10000 training images") dataset. We collected dermatoscopic images from different populations, acquired and stored by different modalities. The final dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions: Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec), basal cell carcinoma (bcc), benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses, bkl), dermatofibroma (df), melanoma (mel), melanocytic nevi (nv) and vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc).More than 50% of lesions are confirmed through histopathology (histo), the ground truth for the rest of the cases is either follow-up examination (follow_up), expert consensus (consensus), or confirmation by in-vivo confocal microscopy (confocal). The dataset includes lesions with multiple images, which can be tracked by the lesion_id-column within the HAM10000_metadata file.
Diseases Detectable:
1. Melanocytic nevi
2. Melanoma
3. Benign keratosis-like lesions
4. Basal cell carcinoma
5. Actinic keratoses
6. Vascular lesions
7. Dermatofibroma