🚀 5 Mistakes I Made in My First CNN Project (That Ruined My Results)

😅 I Thought My Model Was Working… Until It Wasn’t When I built my first CNN model for brain tumor classification using MRI images, I felt confident. The code was running Accuracy looked good Predictions were coming The model classified images into: Glioma Meningioma Pituitary No Tumor Everything seemed fine… until I looked closer. 👉 The model wasn’t learning what I thought it was. Here are the 5 mistakes that taught me more than any tutorial. ❌ Mistake 1: Ignoring Class Distribution I didn’t properly check: How many images per class? Whether all 4 classes were balanced? 👉 Result: The model became biased toward dominant classes. It looked accurate—but struggled on minority classes. 🖼️ Class Imbalance Problem 👉 Lesson: In multi-class problems, imbalance is even more dangerous than binary cases. ❌ Mistake 2: Increasing Model Complexity Without Reason I assumed: “More layers = better classification across all 4 classes” So I kept adding layers. 👉 Result: Training accuracy increased Validat