Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4
Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis
Volume-3 | Issue-1
An Efficient Machine Learning based Model for Classification of Wearable Clothing
Volume-3 | Issue-4
Volume - 1 | Issue - 1 | september 2019
Published
September, 2019
Cancer is a deadly disease that is costing the lives of many people. Over 9.6 million death is reported in 2018 due to cancer. We propose an ideal methodology to identify and classify cancer cells using pathological images with the help of capsule network. Capsule network's capability to learn patterns based on previous iterations can be exploited for this purpose. This can help in identification of cancer at early stages and work at the root cause of the disease and walk towards completely shutting down the disease. Image processing is done along with fuzzification and further, it is handled with capsule network classifier and analysed.
KeywordsImage Processing Capsule Network Artificial Neural Network Cancer detection Fuzzy system
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