Keros-Net: a convolutional block attention module-squeeze-and-excitation-integrated hybrid learning framework for...

This study aimed to develop a hybrid decision support framework combining deep learning (DL) and machine learning (ML) to automatically classify olfactory fossa depth on paranasal computed tomography (CT) images according to the Keros classification. The goal was to enhance accuracy, reduce observer variability, and support safer endoscopic sinus surgery.

A retrospective dataset of 481 individuals (1,549 cropped coronal CT slices) was analyzed and labeled as Keros types I-III by an experienced radiologist. Deep features were extracted using DenseNet121, DenseNet169, and DenseNet201 architectures enhanced with convolutional block attention modules (CBAMs) and squeeze-and-excitation (SE) blocks. Four feature selection techniques-SHapley Additive exPlanations, recursive feature elimination (RFE), principal component analysis, and SelectKBest-were applied to reduce dimensionality. Selected features were classified using support vector machines (SVMs), random forest, XGBoost, logistic regression, and Naive Bayes. Five-fold cross-validation was used to assess accuracy, precision, recall, and F1-score metrics.

Among the baseline models, DenseNet169 achieved the highest accuracy (88.37%). After feature selection, the RFE + SVM and RFE + logistic regression combinations yielded the best performance with an accuracy of 97.90%, demonstrating a substantial improvement over DL models alone. The most effective feature selection technique was RFE, and SVMs consistently produced well-balanced classification results.

Integrating CBAM-SE-enhanced DenseNet architectures with optimized feature selection and classic ML classifiers enables highly accurate and reliable automatic classification of Keros types. The proposed hybrid approach outperforms conventional DL models and provides a robust framework for objective radiological assessment.

Accurate preoperative identification of olfactory fossa depth is essential for preventing complications such as cribriform plate injury and cerebrospinal fluid leakage during endoscopic sinus surgery. The proposed system offers an efficient, reproducible, and objective tool that may enhance surgical planning, reduce operator dependency, and increase patient safety.