Bone Cancer Detection using Image Processing
Bone Cancer Detection Using Segmentation and Classification based on Ensemble Deep Learning Algorithms
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Implementation Plan:
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Step-1: Initially we Load the image from the MRI image dataset.
Step-2: Perform the Two-fold Preprocessing , during this process perform the noise elimination process by using Altered Successive Filter (ASF), and also perform the di-augmentation process by using Enhanced Grasshopper Optimisation Algorithm (EnGRop).
Step-3: Next , perform the feature extraction by using Modified DeeplabV3+ (Mod 2 eep) and also perform the segmentation.
Step-4: Next, Perform the Bone Cancer Detection and Classification process by using three different DL algorithms named Lightweight YOLO5 (LYOLO5), Prediction Fine-tuning CapsNet(PreCap), and Enhanced GoogleNet (EGooNet).
[ The process based on your proposal, Bone Cancer Detection Using Segmentation and Classification based on Ensemble Deep Learning Algorithms ]
Step-5: The performance of the proposed work is measured through the following performance metrics, F1 Score,Accuracy,Sensitivity and Specificity.
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Software Requirement:
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1. Tool: Python - 3.11.3
2. Operating System: Windows 10(64-bit)
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Note: - we perform the existing process based on the reference title:- Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms
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