This dataset is a balanced and augmented version of the original Scoliosis Detection Dataset designed for deep learning and computer vision tasks, particularly spinal curvature classification using YOLOv5.

It contains dermatoscopic-style spine X-ray images categorized into four classes based on the severity of scoliosis:

1-derece → Mild scoliosis

2-derece → Moderate scoliosis

3-derece → Severe scoliosis

saglikli → Healthy (no scoliosis)

⚙️ Data Details

Train set: ../train/images

Validation set: ../valid/images

Test set: ../test/images

Total Classes: 4

Balanced Samples: Each class contains approximately 1259 images and labels

Augmentations Applied:

Rotation

Brightness and contrast adjustment

Horizontal flip

Random zoom and cropping

Gaussian noise

These augmentations were used to improve model robustness and reduce class imbalance.

🎯 Use Cases

This dataset is ideal for:

Scoliosis detection and classification research

Object detection experiments (YOLOv5, YOLOv8, EfficientDet)

Transfer learning on medical image datasets

Model comparison and explainability studies

📊 Source

Originally sourced and preprocessed using Roboflow, then restructured and balanced manually for research and experimentation.

Roboflow Project Link: 🔗 View on Roboflow

🧠 License

CC BY 4.0 — Free to use and share with attribution.

Here are a few use cases for this project:

1. Horror Movie Content Filtering: Thesis YoloV5 can be used by streaming platforms and content providers to identify and categorize horror movies based on the type of horror imagery present, offering tailored content recommendations to users based on their preferred horror subgenres.

2. Video Game Scene Classification: Game developers can use Thesis YoloV5 to analyze video game scenes in horror games, enabling more immersive and dynamic gameplay experiences by adapting game environments, NPC interactions, or difficulty levels according to the detected horror elements.

3. Content Moderation for Online Communities: Online forums, social media platforms, and image sharing sites can utilize Thesis YoloV5 to ensure that users adhere to content policies, automatically moderating and flagging inappropriate horror imagery to maintain a safe and inclusive online community.

4. Augmented Reality Experiences: Thesis YoloV5 can be integrated into AR applications to generate interactive and engaging horror-themed experiences for entertainment, education, or marketing purposes. Users could interact with AI-generated horror characters, solve puzzles based on detected horror elements, or enjoy immersive and personalized storytelling experiences.

5. Horror-centric Art and Design: Artists, graphic designers, and filmmakers can use Thesis YoloV5 to analyze and reference horror imagery for creating unique visual styles, mood boards, and thematic concepts for art, design projects, or marketing campaigns centered around horror themes.

🩻 Scoliosis Spine Detection Dataset (YOLOv5 Ready)

This dataset is a curated and preprocessed version of a Scoliosis Spine X-ray dataset, designed specifically for deep learning–based object detection and classification tasks using frameworks like YOLOv5, YOLOv8, and TensorFlow Object Detection API.

It contains annotated spinal X-ray images categorized into three classes, representing different spinal conditions.

🧩 Dataset Configuration

train: scoliosis2.v16i.tensorflow/images/train
val: scoliosis2.v16i.tensorflow/images/valid
test: scoliosis2.v16i.tensorflow/images/test

nc: 3
names: ['Vertebra', 'scoliosis spine', 'normal spine']

⚙️ Data Details

• Train Set: /images/train
• Validation Set: /images/valid
• Test Set: /images/test
• Total Classes: 3
• Annotations: YOLO format (.txt files with class, x_center, y_center, width, height)
• Image Format: .jpg / .png

Classes Description:

1. Vertebra — Labeled vertebral regions used for bone localization.
2. Scoliosis Spine — X-rays showing curvature or deformity in the spinal structure.
3. Normal Spine — Healthy, straight spinal alignment without scoliosis signs.

🧠 Augmentations Applied

To enhance diversity and model robustness, the dataset was augmented using:

• Rotation
• Brightness and contrast adjustment
• Horizontal flip
• Random zoom and cropping
• Gaussian noise

🎯 Use Cases

This dataset is ideal for:

• Scoliosis detection and classification research
• Vertebra localization and spine anomaly detection
• Medical object detection experiments (YOLOv5, YOLOv8, EfficientDet)
• Transfer learning on medical X-ray datasets
• Explainable AI and model comparison studies

📊 Source

The dataset was preprocessed and labeled using Roboflow, then manually refined and balanced for research use. Originally derived from a spinal X-ray dataset and adapted for deep learning object detection.

Roboflow Project Link: 🔗 View on Roboflow (add your Roboflow link here)

🧾 License

CC BY 4.0 — Free to use, modify, and share with attribution.

Would you like me to make a short summary version (under 1000 characters) for the “Short Description” field on Kaggle too? It’s required for the dataset card.

A dataset of rice diseases consists of ten classes of diseases namely: Bacterial Leaf Streak, Bacterial Blight, Bacterial Panicle Blight, Blast, Brown Spot, Dead Heart, Downy Mildew, False smute, Hispa, and Tungro) and normal rice leaves
The dataset was augmented and divided into 3 parts, which are train folder (44229) image ,test folder (5528) image and valid folder (6000) image The dataset was converted to the YOLO labeling format and supplying one text file for each image's annotations. All images were labeled with two classes (disease, normal) according to its affected or not. The original dataset before labeling was collected from the publically available Kaggle website, the dataset includes 13878 images (10407 training and 3471 testing) Paddy Doctor: Paddy Disease Classification | Kaggle.” https://www.kaggle.com/competitions/paddy-disease-lassification/data (accessed Feb. 13, 2023). Another class (False Smut) was added from the Mendeley website, which includes 44 image P. Sethy, “RICE FALSE SMUT,” vol. 1, 2020, doi: 10.17632/GPSFT4C2ZY.1.