Artificial intelligence and machine learning assignment 1 PPT.pptx

It's about AI/ML

1. Introduction to AI/ML in Healthcare: Artificial Intelligence (AI) and Machine Learning (ML) have spearheaded a paradigm shift in healthcare. These technologies leverage data-driven insights to enhance diagnostics, treatment, and patient care. Introduction to the Five Articles: Article 1: A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks Article 2: CNN Based on Complex Networks for Brain Tumor Image Classification with a Modified Activation Function Article 3: Deep Transfer Learning Models for Brain Tumor Classification Using MRI Article 4:  Brain Tumor Detection and Classification Using Deep Learning Approaches Article 5:  Brain Tumor Detection using the VGG-16 Model: A Deep Learning Approach.

2. Contributions of the Articles: Each article provides distinctive perspectives on harnessing AI/ML technologies specifically in the realm of brain tumor research and treatment. They collectively explore predictive analytics, dataset challenges, disease diagnosis, and precision medicine using advanced computational techniques. Emphasizing the Significance: These articles signify the transformative potential of AI/ML in healthcare, ranging from brain tumor prediction to personalized treatment strategies. Highlighting the crucial role of AI/ML in improving patient outcomes, optimizing treatment decisions, and shaping the future of healthcare delivery.

3. Comparative Analysis of AI/ML in Healthcare Articles: Focus and Scope: Article 1: Concentrates on creating models for the detection and categorization of brain tumours using deep learning. Article 2: CNNBCN with a modified activation function for the MRI classification of brain tumours is presented. Article 3: Focuses on classifying (MRI) images and accurately identify the presence of brain tumours. Article 4: Presents an automatic technique for brain tumour classification from MRI data, using CNN based Squeeze and Excitation ResNet model.  Article 5: This article presents a study on brain tumor detection using the VGG-16 model .

4. Disease or Condition: Brain Tumours : Articles 1,2,3 ,4 and 5

5. Machine Learning Methods: Supervised Learning: Articles 3 Deep Learning: Articles 1, 2 , 4 , 5

6. Application Areas: Risk Prediction: Articles 1, 2 , 4, 5   Early Diagnosis: Articles 1 , 4, 5   Precision Medicine: Article 4, 5

7. Article 1: "Brain Tumor Segmentation and Classification using Texture Features and Support Vector Machine" Pros: High Accuracy Clear Methodology  Utilization of Texture Features Applicability to Real-world Data  Cons: Lack of  Comparison Generalization

8. Article 2: " Densenet201: A Customized DNN Model for Multi-Class Classification and Detection of Tumors Based on Brain MRI Images " Pros: Customized Model Large Dataset Benchmark Datasets Cons: Lack of Comparison with Existing Models  No Insights into Hyperparameter Tuning 

9. Article 3: "Deep Transfer Learning Models for Brain Tumor Classification Using Magnetic Resonance Images" Pros: Utilization of Pre-trained Models  Early Detection and Treatment Support Large and Balanced Dataset Cons Single Metric Evaluation Limited Scope:

10. Article 4: "Brain Tumor Detection and Classification Using Deep Learning Approaches" Pros: High Accuracy Utilization of State-of-Art Architectures Detailed Methodology Cons: Limited Discussion on Hyperparameters Validation of Results  

11. Article 5: "Reinforcement learning for intelligent healthcare applications: A survey" Pros: Established Model Relevance to Healthcare Clear Methodology Cons: Limited Dataset Information Performance Variation on Test Set