An Empirical Evaluation of a Novel Ensemble Deep Neural Network Model and Explainable AI for Accurate Segmentation and Classification of Ovarian Tumors Using CT Images

Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models—KNN, logistic regression, SVM, decision tree, and random forest—resulted in an improved accuracy of 92.8% compared to single classifiers

Lipoma Arborescens of Knee Joint: Role of Imaging

A 23 year old Asian female presented with swelling of right knee joint for 5 years with history of exacerbations and remissions of symptoms. She was initially diagnosed as a case of suprapatellar bursitis based on clinical and X-ray findings. Further evaluation with higher imaging modalities was pathognomonic of lipoma arborescens. Patient underwent synovectomy and the diagnosis was confirmed histologically. We describe a histologically proven case of lipoma arborescens to highlight the imaging findings on X-ray, Ultrasound and Magnetic resonance imaging with arthroscopic correlation. The unique feature of this case report is multimodality imaging correlation with arthroscopy and histopathology findings. We have highlighted the pathognomonic imaging findings of this rare but benign intra-articular lesion and also discussed the differential diagnosis in detail