# Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism

arXiv: https://arxiv.org/abs/2505.21316 · HTML: https://mlsanity.com/paper/2505.21316/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method](https://mlsanity.com/paper/2408.14674/) (2024)
- [Attention Toward Neighbors: A Context Aware Framework for High Resolution Image Segmentation](https://mlsanity.com/paper/2106.12902/) (2021)
- [Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling](https://mlsanity.com/paper/2505.05599/) (2025)

## Built on by (most cited first, 1 of 1)

- [Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis](https://mlsanity.com/paper/2508.04573/) (2025)

## Abstract (arXiv)

Enhancing plant disease detection from leaf imagery remains a persistent challenge due to scarce labeled data and complex contextual factors. We introduce a transformative two-stage methodology, Mid Point Normalization (MPN) for intelligent image preprocessing, coupled with sophisticated attention mechanisms that dynamically recalibrate feature representations. Our classification pipeline, merging MPN with Squeeze-and-Excitation (SE) blocks, achieves remarkable 93% accuracy while maintaining exceptional class-wise balance. The perfect F1 score attained for our target class exemplifies attention's power in adaptive feature refinement. For segmentation tasks, we seamlessly integrate identical attention blocks within U-Net architecture using MPN-enhanced inputs, delivering compelling performance gains with 72.44% Dice score and 58.54% IoU, substantially outperforming baseline implementations. Beyond superior accuracy metrics, our approach yields computationally efficient, lightweight architectures perfectly suited for real-world computer vision applications.