diff --git a/docs/en/datasets/detect/brain-tumor.md b/docs/en/datasets/detect/brain-tumor.md
index 81723239..695e1ec2 100644
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@@ -8,6 +8,17 @@ keywords: Ultralytics, Brain Tumor dataset, object detection, YOLO, YOLO model t
A brain tumor detection dataset consists of medical images from MRI or CT scans, containing information about brain tumor presence, location, and characteristics. This dataset is essential for training computer vision algorithms to automate brain tumor identification, aiding in early diagnosis and treatment planning.
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## Dataset Structure
The brain tumor dataset is divided into two subsets:
diff --git a/docs/en/guides/object-counting.md b/docs/en/guides/object-counting.md
index b3cc55d9..267c48c0 100644
--- a/docs/en/guides/object-counting.md
+++ b/docs/en/guides/object-counting.md
@@ -10,16 +10,28 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Trackin
Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves accurate identification and counting of specific objects in videos and camera streams. YOLOv8 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.
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