LungColonCancerPred-NN: A Neural Network Approach for Lung and Colon Histopathology Prediction

Abstract Lung and colorectal cancers are the first and second causes of cancer death worldwide, and their diagnosis rests on the reading of hematoxylin and eosin (H\&E) stained tissue. The LC25000 dataset is the most used public benchmark for automated classification of lung and colon histopathology, but its $25\,000$ images were produced by augmenting only $1\,250$ source images, so a random split places near copies of the same image in the training and test sets. We present LungColonCancerPred-NN, a compact triple-branch network ($5.8$M parameters) for five-class classification (colon adenocarcinoma, benign colon, lung adenocarcinoma, benign lung, and lung squamous cell carcinoma), and we evaluate it under a protocol designed to keep near copies apart. We combined LC25000 with a second public collection of the same classes ($40\,000$ files), removed $2\,076$ exact duplicates by MD5 hashing, grouped the remaining $37\,924$ images into $7\,795$ augmentation families using an EfficientNet-B0 embedding made invariant to the eight rotations and reflections of the square, and split the data $70/15/15$ by family. An audit of nearest-neighbor similarities confirmed the separation: the median similarity of a test image to its closest training image was $0.961$, against $0.998$ within the training set, and no test image exceeded the grouping threshold of $0.98$. The model shares one EfficientNet-B0 feature map between a convolutional pooling branch, a graph branch that passes messages between similar feature positions, and a Transformer encoder branch, and a sigmoid gate weights the three embeddings before classification. On the held-out test set of $5\,497$ images, LungColonCancerPred-NN reached an accuracy of $99.25\%$ (95\% bootstrap CI $99.02$-$99.49$), a macro-$F_1$ of $99.38\%$ ($99.19$-$99.57$), Cohen's $\kappa$ of $0.9905$ and a macro-AUC of $0.9997$. No malignant image was classified as benign, only two images were assigned to the wrong organ, and $33$ of the $41$ errors, squamous cell carcinomas were predicted as adenocarcinomas. This accuracy is higher than those reported by four recent (2025-2026) CNN, Vision Transformer, and CNN-Transformer methods on LC25000 ($97.00$-$99.11\%$) for the three lung classes alone, while our model handles five classes under a split grouped by augmentation family.