Paper Title
EVALUATING THE CHALLENGES IN CNN-BASED CLASSIFICATION OF ORAL SQUAMOUS CARCINOMA: A REVIEW

Abstract
This review critically evaluates the research paper titled "An Efficient Convolutional Neural Network-Based Classifier for an Imbalanced Oral Squamous Carcinoma Cell Dataset," identifying several key limitations in the methodology, dataset usage, and model architecture. The original study focuses on binary classification using a limited, imbalanced dataset, achieving a reported 99% accuracy. However, this review highlights concerns about overfitting, dataset generalizability, and the simplistic nature of the convolutional neural network (CNN) employed. The lack of multi-class classification, advanced data balancing techniques, and comprehensive preprocessing are significant drawbacks. Recommendations for improvement include adopting more sophisticated sampling methods such as SMOTE, expanding the model to multi-class classification, testing on diverse datasets, and employing deeper neural network architectures. By addressing these limitations, the model's applicability in real-world medical settings could be significantly enhanced, ensuring more robust and generalizable performance in clinical practice Keywords - Oral Squamous Carcinoma (OSCC), Medical Image Classification, Convolutional Neural Network (CNN), Imbalanced Dataset, Overfitting, Binary Classification, Multi-class Classification, SMOTE (Synthetic Minority Over-sampling Technique), Data Augmentation, Deep Learning in Healthcare, Preprocessing Techniques, Model Generalization, Dataset Diversity, Neural Network Architecture, Image Processing in Medical Applications.