EfficientNetB7-Based Deep Learning Framework for Enhanced Classification of Lung and Colon Cancer Histopathological Images

Summary

Here, we introduce a deep learning system with the EfficientNetB7 model for the precise classification of lung and colon cancer histopathological images. The model gained 96% accuracy with the application of preprocessing, data augmentation, and transfer learning. The method has a high prospect for aiding clinical cancer diagnosis.

Abstract

Early diagnosis of lung cancer plays a pivotal role in ensuring improved treatment and survival of patients. This remains a major focus in clinical research. Artificial intelligence (AI) has transformed pathology by significantly improving diagnostic accuracy and efficiency. This study presents a robust deep learning model in the shape of the pretrained EfficientNetB7 model to classify colon and lung tissue histopathological images with an extremely high accuracy of 96%. The model's performance was optimized using advanced preprocessing methods, fine-tuning, and domain-specific data augmentation techniques. These strategies help reduce problems such as class imbalance and subtle histological variations. To address the issue of overfitting, multiple data augmentation techniques were combined, and an early stopping criterion was incorporated. This approach enabled efficient and cost-effective training. Robust validation of the model demonstrates high utility for clinical applications and enables pathologists to deliver timely and accurate diagnoses. Integrating advanced deep learning models into medical imaging workflows holds great promise for early and accurate cancer diagnosis, ultimately improving patient outcomes.

Introduction

Lung and colon cancer are among the most prevalent cancers in the world in terms of mortality. Lung cancer is the leading fatal cancer with over 1.8 million deaths annually, followed by colon cancer as the third most occurring malignancy and the second most common cause of cancer mortality, based on global health statistics. Accurate and early diagnosis is crucial for effective treatment and improved survival of these cancers. Histopathological examination, or microscopic evaluation of tissue samples by pathologists, remains one of the most frequent methods of detecting cancer¹. Figure 1 shows the sample histopatho....

Protocol

This study did not involve any direct experimentation on human participants or animals. All work was conducted using the publicly available, anonymized LC25000 dataset of histopathological images, which contained no identifiable patient information or direct handling of human tissue. Institutional Review Board (IRB) or Institutional Animal Care and Use Committee (IACUC) approval was not required. All procedures complied with ethical standards and adhered to the dataset's terms of use for academic research. Figure 2 shows the steps of the workflow diagram.

Results

Figure 4 presents the training and validation Accuracy. Figure 5 presents the training and validation Loss.

Figure 4: Training and validation accuracy over epochs. This figure depicts the progression of accuracy for both training and validation sets across al.......

Discussion

In the critical review of mislabeled instances under the EfficientNetB7 deep learning architecture, a critical examination is carried out on instances where model predictions do not match real labels within the validation dataset. Critical analysis is of extreme importance in analyzing certain errors of classification, particularly when the model misclassifies various histopathological features of lung and colon tissues¹¹. The procedure is to make class predictions on all the images in the validat.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
A100 GPU (CUDA)	NVIDIA	CUDA Version 11.0	GPU acceleration for model training and evaluation.
Kaggle Platform	Google	N/A	Cloud based Notebook for Machine Learning Model Development
Keras	TensorFlow (Google)	Version 2.6.0	Deep learning API running on top of TensorFlow.
LC25000	Borkowski AA, Bui MM, Thomas LB, Wilson CP, DeLand LA, Mastorides SM. Lung and Colon Cancer Histopathological Image Dataset (LC25000)	N/A	This dataset contains 25,000 histopathological images with 5 classes. All images are 768 x 768 pixels in size and are in jpeg file format.
Matplotlib	Python Software Foundation	Version 3.5.0	Visualization library for plotting results.
NumPy	Python Software Foundation	Version 1.19.5	Numerical computing library.
OpenCV	Open Source	Version 4.5.4	Image processing and computer vision library.
Pandas	Python Software Foundation	Version 1.3.4	Data analysis and manipulation tool.
Python (Anaconda Distribution)	Anaconda Inc	Version 3.7.12	Includes pre-installed packages and environment management tools.
Scikit-learn	Python Software Foundation	Version 0.23.2	Machine learning tools for performance evaluation.
TensorFlow	Google	Version 2.6.2	Deep learning framework for diffusion models.