Deep Learning Applied to Chest Medical Images Classification

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General Introduction Chapter 1: Computer-Aided detection and diagnosis in medical imaging of chest diseases Introduction Chest diseases Pneumonia Definition The type of pneumonia The symptoms of pneumonia Tuberculosis Definition The symptoms of TB Lung cancer Definition The types of lung cancer The symptoms of lung cancer Corona virus Definition The symptoms of corona virus Lung opacity Definition The symptoms of lung opacity The medical imaging Medical imaging Modalities of medical images X-ray Ultrasound Computed tomography (CT) Magnetic resonance imaging (MRI) Position emission tomography Computer-Aided Detection and Diagnosis technology CAD in medical imaging Applications of CAD system The work flow of CAD Preprocessing Segmentation Features extraction Features selection Features classification Artificial vision Image processing The Digital Image Acquisition of images Characteristics of a digital image Size Resolution Noise Histogram Luminance Grayscale images Images in color Contrast Histogram equalization Conclusion Chapter 2: Artificial intelligence Introduction Artificial intelligence Definition Types of artificial intelligence Application of artificial intelligence in medicine AI in disease detection and diagnosis Personalized disease treatment AI in medical imaging Machine learning Definition Machine learning methods Supervised machine learning Unsupervised machine learning Reinforcement learning Semi-supervised learning Neural Networks Biological Neural Networks Artificial Neural Network History of ANN Definition ANN VS BNN Activation Functions Optimizers Gradient Descent Stochastic Gradient Descent (SGD) Adam (Adaptive Moment Estimation) RMSprop Construction of a neural network Layers of a neural network Deep learning Definition The difference between Convolutional neural networks The different layers of a CNN Convolutional Layer Pooling Layer The ReLU correction layer Fully-Connected Layer Architecture of a convolutional neural network CNN known models Transfer learning Conclusion Chapter 3: State of Art Introduction Related works Conclusion Chapter 4: Experiments and realization Introduction Tools and libraries Python Google Colaboratory Anaconda Jupyter Notebook TensorFlow Keras Flask Exploited datasets and pre-trained models Datasets Model architecture and development Implementation and training Results Discussion Conclusion General Conclusion

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