Dermato

Template project: The technical details below are illustrative placeholders. They will be replaced with the real dataset, experiments, screenshots, and test results.

Abstract

Dermato is a proposed computer-vision application for assisting with the classification of dermatology images. It is designed as an educational project, not a diagnostic tool. The finished notebook will document the data, model decisions, evaluation, interface, and limitations.

Problem Definition

Can a small image-classification pipeline distinguish between a defined set of skin-lesion categories while clearly communicating uncertainty and avoiding unsafe medical claims?

The project should be evaluated as an assistive research prototype. It must not present a prediction as a diagnosis, and it must direct users to a qualified medical professional.

Proposed System

Image upload → validation → preprocessing → model inference → confidence + guidance
                    └──────────── evaluation and error logging ────────────┘

Data Pipeline

Placeholder for: dataset source, inclusion criteria, class balance, train/validation/test split, image dimensions, and privacy considerations.

image = load_image(upload)
image = resize(image, (224, 224))
image = normalize(image)
probabilities = model.predict(image)

Model Experiment

Placeholder for: baseline model, transfer-learning model, augmentation strategy, optimizer, learning rate, batch size, and number of epochs.

The real page should compare several controlled experiments instead of presenting only the best result. Reproducibility matters more than a single impressive number.

Interface Prototype

Placeholder for: screenshots of the upload flow, loading state, result card, confidence explanation, and medical disclaimer.

DERMATO INTERFACE / SCREENSHOT PLACEHOLDER

Evaluation Plan

The finished project will report class-level precision, recall, F1 score, confusion matrices, and examples of incorrect predictions. Accuracy alone is not sufficient for a medical-image prototype.

Held-Out Test Set

Purpose: Estimate generalization.

Result: Placeholder.

Confusion Matrix

Purpose: Identify class-specific errors.

Result: Placeholder.

Calibration Check

Purpose: Compare confidence to correctness.

Result: Placeholder.

Usability Review

Purpose: Find confusing or unsafe UI copy.

Result: Placeholder.

Safety And Limitations

This prototype cannot replace a clinician. The final page will describe dataset limitations, demographic bias risks, image-quality failures, distribution shift, false negatives, and the difference between research classification and medical diagnosis.

Reproduction

• Repository: View the project on GitHub
• Environment: To Be Added
• Dataset: To Be Added
• Training Command: To Be Added
• Model Weights: To Be Added

Results And Next Steps

This section will contain the actual experiment table, error analysis, and decisions for the next iteration. Every conclusion should point back to a test, a plot, or a reproducible observation.

Revision Checklist

• [ ] Document the dataset and its license.
• [ ] Add preprocessing and training code.
• [ ] Add interface screenshots.
• [ ] Report class-level evaluation metrics.
• [ ] Add a clear limitation and safety statement.
• [ ] Link reproducible commands and model artifacts.

Documentation Status: Draft Template.