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Prash2410/
customresidualcnn_lungcancerclassification

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Lung Cancer Classification using a Custom Residual CNN (PyTorch)

Project Overview

This project presents a deep learning framework for multi-class lung cancer classification from histopathological images using a custom Residual Convolutional Neural Network (ResCNN) implemented entirely in PyTorch.

The model distinguishes between healthy lung tissue and two major lung cancer subtypes while leveraging ResNet-inspired residual learning to improve optimization stability, gradient propagation, and convergence speed. The objective is to establish a lightweight yet effective baseline for automated lung cancer diagnosis from histopathological data.

Problem Statement

Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Histopathological examination serves as the gold standard for diagnosis; however, manual analysis is both time-intensive and highly dependent on expert interpretation.

This project aims to develop an automated deep learning system capable of classifying histopathological lung tissue images into multiple categories, thereby supporting computer-aided diagnosis and reducing diagnostic workload.

Dataset

The dataset consists of three classes:

Class Label	Description
`lung_n`	Normal Lung Tissue
`lung_aca`	Lung Adenocarcinoma
`lung_scc`	Lung Squamous Cell Carcinoma

Dataset: <<INSERT DATASET LINK>>

Data Preparation & Training Pipeline

Data Preparation

The following preprocessing workflow was adopted:

Organized the dataset into training and validation splits.
Applied image normalization using PyTorch transforms.
Built efficient DataLoader pipelines for batched processing.
Preserved class integrity throughout experimentation.
Maintained reproducibility through consistent preprocessing procedures.

Model Development

A custom residual architecture was designed with the following structure:

Progressive feature extraction: 3 → 8 → 16 → 32 → 64 → 128 channels.
Residual learning through skip connections.
Projection shortcuts (1×1 convolutions) whenever feature dimensions changed.
Batch Normalization after every convolutional layer.
Global Average Pooling for parameter efficiency.
Fully-connected classifier with dropout regularization.

Training Configuration

Component	Configuration
Framework	PyTorch
Optimizer	Adam
Initial Learning Rate	`1e-4`
Scheduler	ReduceLROnPlateau
Loss Function	CrossEntropyLoss
Dropout	`0.2`
Epochs	`20`

The learning rate schedule evolved as follows:

1e-4 → 5e-5 → 2.5e-5 → 1.25e-5 → 6.3e-6 → 3.1e-6

This adaptive strategy enabled smoother convergence during later stages of training.

Model Architecture

The proposed network consists of five residual stages followed by a lightweight classification head.

text

Input Image
      ↓
Residual Block (3 → 8)
MaxPool(2)

Residual Block (8 → 16)
MaxPool(2)

Residual Block (16 → 32)
MaxPool(2)

Residual Block (32 → 64)
MaxPool(2)

Residual Block (64 → 128)
MaxPool(2)

Adaptive Average Pooling (1×1)

Flatten

Linear (128 → 256)
BatchNorm
ReLU
Dropout (0.2)

Linear (256 → 128)
BatchNorm
ReLU
Dropout (0.2)

Linear (128 → 3)

Residual Learning

The model incorporates ResNet-inspired residual blocks:

Output = F(x) + x

where:

F(x) denotes the learned residual mapping.
x denotes the shortcut connection.

Whenever channel dimensions change, projection shortcuts using 1×1 convolutions and Batch Normalization are employed.

Benefits of Residual Connections

Faster convergence during optimization.
Improved gradient flow across deeper layers.
Reduced vanishing-gradient effects.
Easier training of deeper architectures.
Better feature reuse and representation learning.
Increased training stability.

The inclusion of skip connections played a significant role in achieving strong performance while maintaining a lightweight model footprint.

Results

Best Performance Metrics

Metric	Value
Maximum Validation Accuracy	96.46%
Best Validation Loss	0.1268
Best Epoch	12
Overall Classification Accuracy	95.00%
Macro F1-Score	0.95
Weighted F1-Score	0.95

Training Performance

The network demonstrated rapid and stable convergence, exceeding 95% validation accuracy while maintaining low validation loss.

The best-performing checkpoint was obtained at:

text

Epoch               : 12
Validation Accuracy : 96.46%
Validation Loss     : 0.1268

Training Curves

Training Loss

Figure 1: Training loss across epochs.

Testing Loss

Figure 2: Testing loss across epochs.

Testing Accuracy

Figure 3: Test accuracy across epochs.

The combination of:

Residual learning,
Batch Normalization,
Adaptive learning-rate scheduling,
Global Average Pooling, and
Dropout regularization,

contributed significantly to efficient optimization and strong generalization.

Classification Report

Class	Precision	Recall	F1-Score	Support
`lung_n`	0.93	0.93	0.93	164
`lung_aca`	1.00	1.00	1.00	167
`lung_scc`	0.93	0.93	0.93	149

Overall Metrics

Metric	Score
Accuracy	0.95
Macro Average	0.95
Weighted Average	0.95

Confusion Matrix Analysis

Confusion Matrix

Figure 4: Confusion Matrix.

True Class	Correct Predictions	Total Samples	Class Accuracy
`lung_n`	153	164	93.29%
`lung_aca`	167	167	100.00%
`lung_scc`	138	149	92.62%

Key Observations

Lung Adenocarcinoma (`lung_aca`)

The model achieved perfect classification performance for adenocarcinoma samples:

167 / 167 correctly classified
No false positives
No false negatives
Precision = 1.00
Recall = 1.00
F1-score = 1.00

This demonstrates exceptionally strong feature extraction capabilities and complete separation from the remaining classes.

Lung Squamous Cell Carcinoma (`lung_scc`)

The model correctly classified:

138 / 149 samples

Misclassifications:

11 samples predicted as normal tissue.
No confusion with adenocarcinoma samples.

This indicates strong subtype-specific feature learning and robust cancer-class separation.

Normal Lung Tissue (`lung_n`)

The model correctly classified:

153 / 164 samples

Misclassifications:

11 samples predicted as squamous cell carcinoma.
No samples predicted as adenocarcinoma.

The absence of confusion with adenocarcinoma highlights the effectiveness of the learned feature representations.

Overall Interpretation

The confusion matrix reveals several important characteristics:

Zero confusion involving adenocarcinoma samples.
Misclassifications occur exclusively between normal tissue and squamous cell carcinoma.
Both cancer classes remain distinctly separated.

Achieving perfect adenocarcinoma detection alongside more than 92% class accuracy on the remaining categories demonstrates the effectiveness of residual learning for histopathological image classification.

Model Availability

Dataset: Google Drive Dataset

Hugging Face Model Repository: <<INSERT MODEL LINK>>

Future Work

Several directions can further enhance this work:

Data Augmentation

Future experiments will explore:

Random rotations
Horizontal and vertical flips
Color jittering
Elastic transformations
Stain normalization methods

Increasing sample diversity may further improve robustness and reduce confusion between normal tissue and squamous cell carcinoma.

Advanced Training Strategies

Potential improvements include:

MixUp and CutMix augmentation.
Cross-validation experiments.
Class-balanced sampling.
Label smoothing techniques.
Attention modules such as CBAM and SE blocks.
Transfer learning from large-scale medical imaging models.

Model Interpretability

Future work may also incorporate:

Grad-CAM visualizations.
Feature activation analysis.
Explainable AI (XAI) methods for clinical interpretability.

Deployment

Planned deployment directions include:

Publishing pretrained weights on Hugging Face.
Building an interactive inference interface.
Packaging lightweight deployment pipelines for research and educational use.

Tech Stack

Python
PyTorch
Torchvision
NumPy
Pandas
Matplotlib
Scikit-learn

Conclusion

This project demonstrates that a lightweight, custom-designed Residual CNN can effectively classify lung histopathological images with strong generalization and excellent class-wise performance.

By incorporating residual learning, batch normalization, adaptive optimization, global average pooling, and regularization techniques, the model achieved:

96.46% peak validation accuracy
0.1268 best validation loss
100% adenocarcinoma detection (167/167 samples)
95% overall classification accuracy
0.95 macro and weighted F1-scores

These results highlight the potential of residual architectures for medical image analysis and establish a strong foundation for future work involving larger datasets, advanced augmentation strategies, explainable AI techniques, and deployment-oriented applications.

Prash2410/customresidualcnn_lungcancerclassification