This study evaluates four well-known convolutional neural networks: VGG19, EfficientNetB0, ResNet50, and InceptionV3, for tumor detection in lymph node pathology images. Using a significant subset of the PCam dataset, models were trained on binary classification tasks focused on identifying tumor tissue, with data augmentation applied to enhance generalization. The methodology involved a rigorous process of data preparation, model selection, training, and evaluation under limited hardware resources, using a standard laptop. The dataset was split into training and validation sets with an 80/20 ratio, and models were trained using the Adam optimizer with a learning rate of 0.001 over multiple epochs. VGG19 achieved the highest validation accuracy at 77.38% and AUC of 85.35% but required substantial computational time and exhibited overfitting. EfficientNetB0, though faster to train (20 m 40 s), showed lower validation accuracy (58.91%) and AUC (60.14%). ResNet50 performed well during training but faced generalization challenges. InceptionV3 demonstrated a balanced performance with a validation accuracy of 70.74% and AUC of 76.41%, making it a promising option across varied datasets. These findings highlight the strengths and limitations of different CNN architectures in enhancing cancer diagnosis in lymph node pathology, providing insights for future research and clinical applications.