Detecting pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to previous systems and research. In this study, we have developed an innovative lung cancer detection system known as the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A dataset, IQ-OTH/NCCD - Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall accuracy of 98.54%, while the classification precision among the three classes was 98.63%. Considering the accuracy, recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.
Automatic handwriting recognition is a fundamental component of various applications in various fields. During the last three decades, it has become a challenging issue that has attracted much attention. Latin language handwriting recognition has been the primary focus of researchers. As for the Kurdish language, only a few researches have been conducted. This study uses a Kurdish character dataset, which contains 40,940 characters written by 390 native writers. We present an ensemble transfer learning-based model for automatically recognizing handwritten Kurdish letters using Densenet-201, InceptionV3, Xception, and an ensemble of these pre-trained models. The model’s performance and results obtained by the proposed ensemble model are promising, with a 97% accuracy rate, outperforming other studies on Kurdish character recognition.