IOP Conference Series Materials Science and Engineering · 2020 · 155 citations · 29 references
MedicineComputer ScienceRadiologyComputer VisionDiagnostic RadiologyMachine LearningCancer DetectionCancer DiagnosisCancer Early DetectionComputer-aided DiagnosisEarly Cancer DetectionLung CancerArtificial Intelligence For DetectionComputer-aided Tumor DiagnosisConvolutional Neural Network DetectionConvolutional Neural NetworksDeep Learning In RadiologyDeep Learning-based DiagnosisEarly DiagnosisRadiology Artificial Intelligence
Abstract Lung cancer is one of the most lethal cancer types; thousands of peoples are infected with this type of cancer, and if they do not discover it in the early stages of the disease, then the chance of surviving of the patient will be very poor. For the suggested reasons above and to help in overcoming this terrible, early diagnosis with the assistance of artificial intelligence procedures most needed. Through this research, a Computer-aided system introduced for detecting lung cancer in a dataset collected from the Iraqi hospitals by using a convolutional neural network technique with AlexNet architecture for helping with the diagnosis of the patient’s cases: normal, benign, or malignant. The proposed model gives high accuracy ups to 93.548%. The other performance metrics comes with high values such as 95.714% for sensitivity and 95% for Specificity.
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