Create README.md

README.md

@@ -0,0 +1,97 @@
+---
+license: apache-2.0
+---
+# Thyroid Ultrasonograph Image Classifier
+
+**Author:** Afif Ali Saadman
+**Type:** Deep Learning (Modified AlexNet variant)
+**Framework:** PyTorch
+**Date:** October 2025
+
+## Overview
+
+**This model was developed as part of an **independent research project** focused on classifying thyroid ultrasound images into multiple diagnostic categories using deep learning.**
+
+
+The model can identify:
+
+- **FTC** – Follicular Thyroid Carcinoma
+- **PTC** – Papillary Thyroid Carcinoma
+- **MTC** – Medullary Thyroid Carcinoma
+- **Benign** – Non-cancerous thyroid tissue
+
+
+## Architecture
+
+NOTE: This model was trained on a T4 GPU in google colaboratory.
+
+* Model : This model was trained on a AlexNet like architecture with gradient checkpointing.
+* Loss Function: Cross Entropy Loss
+* Learning Rate: 0.0001
+* Iters(Epochs): 20
+
+## Dataset
+
+This dataset  was extracted from `FangDai/Thyroid_Ultrasound_Images `and `agent593/Thyroid-Ultrasound-Image-Classification-ViTModel/tree/main/dataset%20thyroid/` which were cleaned manually.
+
+1. **FTC (Follicular Thyroid Carcinoma) – 100 images**
+2. **PTC (Papillary Thyroid Carcinoma) – 99 images**
+3. **MTC (Medullary Thyroid Carcinoma) – 99 images**
+4. **Benign (Normal Thyroid) - 90 images**
+
+## Confusion Matrix
+
+![Confusion Matrix](matrix.png)
+
+
+## Classification Report
+
+
+
+| Class            | Precision | Recall | F1-Score | Support |
+| ---------------- | --------- | ------ | -------- | ------- |
+| FTC              | 0.93      | 0.93   | 0.93     | 15      |
+| PTC              | 0.88      | 0.70   | 0.78     | 10      |
+| MTC              | 0.80      | 0.80   | 0.80     | 10      |
+| Benign           | 0.88      | 1.00   | 0.94     | 15      |
+| **Accuracy**     | -         | -      | 0.88     | 50      |
+| **Macro Avg**    | 0.87      | 0.86   | 0.86     | 50      |
+| **Weighted Avg** | 0.88      | 0.88   | 0.88     | 50      |
+
+
+
+## Final Report
+
+Benign: perfect classification (15/15)
+
+FTC: only one misclassified
+
+PTC: 2 misclassified (one as FTC, one as Benign)
+
+MTC: also strong, only a few mislabels
+
+
+## More information
+
+For more information, kindly see this notebook:[USGResearch.ipynb · Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model at main](https://huggingface.co/Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model/blob/main/USGResearch.ipynb)
+
+
+## Where you can find this model?
+
+**HuggingFace**:[Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model · Hugging Face](https://huggingface.co/Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model)
+
+Kaggle: [Afif Ali Saadman | Thyroid\_Canciroma\_Classifier | Kaggle](https://www.kaggle.com/models/afifalisaadman/thyroid-canciroma-classifier/)
+
+
+## Citation
+
+```
+@misc{saadman2025thyroid,
+  author = {Afif Ali Saadman},
+  title = {Thyroid Ultrasonograph Image Classifier},
+  year = {2025},
+  month = {October},
+  note = {Deep Learning (Modified AlexNet variant), PyTorch. Available at \url{https://huggingface.co/Afifsudoers/Thyroid-Canciroma-Image-Classifier-Model} and \url{https://www.kaggle.com/models/afifalisaadman/thyroid-canciroma-classifier/}}
+}
+
+```