Lung Cancer HistopathologyFour CNNs compared on LC25000
Four convolutional architectures evaluated head-to-head on the LC25000 lung histopathology subset — Modified AlexNet, Modified CNN, EfficientNetB4, and DenseNet-121.
What it had to solve.
Histopathology slide classification is sensitive to small architectural choices — filter sizes, depth, batch normalisation placement. The brief was to evaluate four architectures on a single split, including two built from scratch (a Modified CNN and a Modified AlexNet) and two ImageNet-pretrained backbones (EfficientNetB4, DenseNet-121). The interesting question wasn't 'can we hit state-of-the-art' but 'how do small modifications and transfer learning compare when everything else is held constant?'
Pipeline, end-to-end.
Standard CV preprocessing fed into four CNN architectures, evaluated on a single shared train/val/test split. The headline came from a Modified AlexNet variant; a Modified CNN trained from scratch, EfficientNetB4, and DenseNet-121 made up the comparison set.
What the comparison actually said.
Comparative results
All four models evaluated on the same held-out test split of LC25000 (lung subset, ~2,100 test images, 700 per class). Modified AlexNet led the comparison; the three other architectures sat within ~3 percentage points of each other.
Choices that shaped the comparison.
Held the split fixed across all models
Same pre-defined train / validation / test folders used for every architecture. Comparing accuracies on different splits silently rewards luck — the comparison only meant something with the split frozen.
Same input footprint for every model
64×64 normalised inputs across all four architectures so every model trained on the same pixel budget. Light enough to iterate quickly on academic compute; honest enough that the comparison stayed apples-to-apples.
Reported per-class metrics, not just top-line accuracy
Precision, recall, and F1 per class, plus confusion matrices and ROC curves. The architectures agreed on the easy classes and diverged on the boundary cases — that's where any honest discussion has to start.
Trained-from-scratch alongside transfer learning
Modified CNN and Modified AlexNet were trained from scratch; EfficientNetB4 and DenseNet-121 used frozen ImageNet backbones with a small trainable head. Keeping both styles in the comparison kept the conclusions grounded.