Enhanced Histopathologic Image Analysis for Mouth Cancer Classification Using Morphological Reconstruction and UNet

Mouth cancer represents a significant global public health challenge due to its high incidence rate and potentially fatal outcomes if not diagnosed early. Among the various types, oral cancer is notably prevalent and poses considerable diagnostic complexity due to its intricate histopathological architecture. According to the World Health Organization and recent epidemiological studies, oral cancer accounts for approximately 377,000 new cases and 177,000 deaths annually worldwide. Traditional diagnostic methodologies such as manual clinical inspection and biopsy analysis are often time-intensive, inherently subjective, and susceptible to inter-observer variability among pathologists. To address these limitations, this study proposes novel framework, termed Depthwise Separable Convolution U-Net (DWSU-Net), aimed at enhancing the accuracy and efficiency of mouth cancer detection. Unlike traditional U-Net, which employs computationally expensive standard convolutional layers, DWSU-Net integrates depthwise separable convolutional blocks into both encoder and decoder stages, reducing parameter count and training complexity while preserving representational power. This makes the model more lightweight, scalable, and suitable for real-time or resource-constrained clinical environments. The research utilizes histopathologic images of oral tissues obtained from the publicly available oral cancer detection dataset on Kaggle, which were captured using a Leica ICC50 HD microscope. The proposed approach initiates with a comprehensive data preprocessing pipeline involving multiple filtering techniques. Raw histopathological images are transformed using Sobel filtering, Otsu thresholding, Canny edge detection, and morphological reconstruction via erosion, thereby improving feature saliency and contrast in malignant regions. The preprocessed dataset is partitioned into training, validation, and testing subsets in an 80:10:10 ratio and evaluated using fivefold cross-validation to ensure the robustness and generalizability of the model. A comparative analysis was conducted using conventional CNN architectures to identify the most effective combination of model and filtering technique. Empirical results indicated that MobileNet and U-Net, when applied to images filtered through morphological reconstruction by erosion, yielded superior classification performance. Motivated by these findings, the proposed DWSU-Net architecture was developed by integrating the strengths of MobileNet and U-Net. The novelty of the DWSU-Net model lies in morphological reconstruction-based preprocessing, which enhances interpretability by making malignant regions more distinct for both the algorithm and pathologists, thereby bridging the gap between AI predictions and clinical understanding. By combining efficiency, robustness, and interpretability, DWSU-Net goes beyond accuracy gains, offering a clinically relevant decision-support framework. Experimental evaluations demonstrate that the proposed model achieves an outstanding classification accuracy of 99.74% in distinguishing between healthy and malignant oral tissue images, underscoring its potential for clinical deployment in automated cancer diagnostics.