Bone Cancer Detection using Deep Learning Research Topics

In the wide area of Machine Learning (ML), Deep Learning is a branch of it. This technique is popular because of its multiple layer filtering. By going through this research you can get a better understanding of the topic in detail. Continue reading this research paper to gain more knowledge about Deep learning.

  1. Define Deep Learning Algorithms

Deep learning is one of the methods employed by ML, which is structured based on the functioning of neural network present in the human brain. This technique uses several layers of nodes which are interconnected to each other in order to understand about representing data in a hierarchical order, just like a neural network that is made artificially. Deep learning technique is mainly used in the area of Natural language Processing (NLP), pattern recognition also speech and image recognition. For training this model, back propagation is used for adjusting various parameters. This will help in minimizing errors by making necessary updates to the weight of loss function gradient. The two main systems which use this type of architecture are Recurrent Neural Networks (RNNs) in sequential data and “Convolutional Neural Networks” (CNNs) in image processing.

  1. What is Deep Learning Algorithms?

This technique is a sub-section of ML which is used to train the artificial neural network by using multiple layers. This technology is built based on the function and structure of a human brain which has similar function of processing through multiple layers of interconnected neurons. Deep learning became more popular in several fields because of its capability to obtain meaningful content with only raw data that too automatically.

  1. Where Deep Learning Algorithms is used?

These deep learning algorithms are used in areas like computer vision, image recognition, facial detection, NLP, sentiment analysis and language translation. It is also used in healthcare for analyzing medical image and doing diagnosis. Deep Learning is more important for autonomous systems like robotics, autonomous vehicles and informed decision-making.

  1. Why Deep Learning Algorithms is proposed? Previous Technology Issues

Moving on to the next section, here we are going to discuss about why this technique is proposed and about the challenges faced by it. This was proposed in order to overcome the issues face by earlier techniques of machine learning. Some of the issues faced by it are mentioned here:

Ineffective pre-processing: Maintaining accuracy while doing classification of medical image depends on the quality of the image. In other words, when removing noise by making use of any ill-suited filters, it may reduce the quality of image.

Poor Segmentation: Segmentation should be done effectively with the help of perfect algorithm and by considering suitable features. When the segmentation is not done properly like not selecting effective approaches or doing manual segmentation, it may lead to poor system performance.

High false positive rate: In order to classify bone cancer there is need of some features like texture and edge from any recent work; but that is not enough for effective classification of bone cancer. So, the system will produce only highly false positive result ratio.

  1. Algorithms / Protocols

After knowing about the technology, uses of it and the issues faced by them in the earlier stage, now we are going to learn about the algorithms used for this technology. The algorithms provided for Deep Learning to overcome the previous issues faced by it are: “Enhanced Grasshopper Optimization Algorithm” (EnGRop), “Prediction Fine-tuning CapsNet” (PreCap), “Modified DeeplabV3+”, “Enhanced Google Net” (EGooNet), “SubGrade APSP”, “Altered Successive Filter” (ASF) and “Lightweight YOLO5” (LYOLO5).

  1. Simulation results / Parameters

The approaches which were proposed to overcome the issues faced by Deep Learning in the above section are tested using different methodologies to analyze its performance. The comparison is done by using metrics like Accuracy, Specificity, Sensitivity and F1 Score.

  1. Dataset LINKS / Important URL

Here are some of the datasets and link provided for you below to gain more knowledge about Deep Learning which can be useful for you:

  1. Deep Learning Algorithms Applications

In this next section we are going to discuss about the applications of Deep Learning technology. This technology has been employed in many industries, from which some of them are listed here: It is used in computer vision for facial and image recognition, NLP, sentiment analysis and Language translation, also useful in healthcare industries for diagnosing disease and analyzing image. Industries like finance, marketing and autonomous system uses deep learning for its predictive capabilities like pattern recognition and decision-making.

  1. Topology

Here you are going to learn about the different choices of topologies which can be used in Deep Learning technology. Generally topology refers to methodology and architecture design of the system. Here in this case, topology refers to arrangement of algorithm and steps involved in it, starting from loading image to feature extraction, preprocessing till applying the algorithms of deep learning. A clear topology will help you improve your workflow and efficiency.

  1. Environment

For a better performance of the system, it needs a suitable environment. Environment means the conditions and criterions required for conducting a research, the factors in it includes data sources, technological or medical settings and imaging technologies. To produce a well-defined result with more robustness and applicability; understanding those environmental conditions are very much important.

  1. Simulation Tools

Here we provide some simulation software for Deep Learning system, which is established with the usage of Python tool version 3.11.4 and along with MATLAB R2020b.

  1. Results

After going through this research based on Deep Learning Technology, you can understand in detail about this technology, applications of this technology, different topologies of it, algorithms followed by it also about the limitations and how it can be overcome.

Bone Cancer Detection using Deep Learning Research Ideas

  1. Multi-Level Fusion of Multi-Source Information Based Deep Learning and Ensemble Deep Learning Models
  2. The Study of Deep Learning in Brain Tumor and Intracranial Hemorrhage Detection
  3. Dynamics Personalized Learning Path Based on Triple Criteria using Deep Learning and Rule-Based Method
  4. Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review
  5. Overview of Bearing Fault Diagnosis Based on Deep Learning
  6. Deep vs. Shallow: A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection
  7. Sleep Apnea Detection from Single-Lead ECG A Comprehensive Analysis of Machine Learning and Deep Learning Algorithms
  8. Study of Machine Learning and Deep Learning Algorithms for the Detection of Email Spam based on Python Implementation
  9. Deep Learning Techniques in Digital Clinical Diagnostic System for Lung Cancer
  10. A Study on Digital Pathology Image Segmentation Using Deep Learning Methods
  11. Applications of Deep Learning in Medical Imaging: A Brief Review
  12. A Comparative Study on Emotion AI using Machine Learning and Deep Learning Models
  13. Intelligent IoT-based Combined Crop-type and Disease Prediction System with Different Machine Learning & Deep Learning Techniques
  14. Offloading Mechanisms Based on Reinforcement Learning and Deep Learning Algorithms in the Fog Computing Environment
  15. A Self-Supervised Deep Learning Method for Seismic Data De-blending Using a Blind-Trace Network
  16. Development of PM Collection Efficiency Prediction Model Based on Deep Learning to Improve WESP PM Collection Performance
  17. Analysis of Deep Learning and Machine Learning Methods for Breast Cancer Detection
  18. Hybrid Detection: Enhancing Network & Server Intrusion Detection Using Deep Learning
  19. Machine Learning and Deep Learning Techniques on Accurate Risk Prediction of Coronary Heart Disease
  20. Comparative Analysis of Traditional Machine Learning and Deep Learning Techniques for Facial Expression Recognition
  21. Post-Stroke Virtual Assessment Using Deep Learning
  22. An Improved System for Brain Pathology Classification using Hybrid Deep Learning Algorithm
  23. Pneumonia Identification Using Deep Learning Models
  24. Enhancing Lung Cancer Detection and Classification Using Machine Learning and Deep Learning Techniques: A Comparative Study
  25. A Review on the Application of Radiomics and Deep Learning for Disease Identification in Musculoskeletal Radiography
  26. A Study of Deep Learning and Blockchain-Federated Learning Models for Covid-19 Identification Utilizing CT Imaging
  27. Deep Learning Based Cyber Attack Detection in 6G Wireless Networks
  28. Image Classification for Optimized Prediction of Leukemia Cancer Cells using Machine Learning and Deep Learning Techniques
  29. Comparative Analysis of Deep Learning and Machine Learning Models for Burned Area Estimation Using Sentinel-2 Image: A Case Study in Muğla-Bodrum, Turkey
  30. Enhanced Complex Human Activity Recognition System: A Proficient Deep Learning Framework Exploiting Physiological Sensors and Feature Learning
  31. The Study of Traditional Pest Image Recognition and Deep Learning Pest Image Recognition
  32. An Overview of Bio-Inspired and Deep Learning Model for Extraction of Land Use Pattern
  33. Deep learning knowledge tracing based on behavioral features
  34. Pneumonia Classification Using Deep Learning VGG19 Model
  35. Detection of Tuberculosis Disease Using Deep Learning Techniques
  36. Design and Application of Artificial Intelligence GIS Algorithm Based on Deep Learning Technology
  37. Elderly Fall Detection using Deep Learning Techniques
  38. Comparative Analysis of Machine Learning and Deep Learning Techniques in Text Based Emotion Detection
  39. Real-Time Face Mask Detection in Video Streams Using Deep Learning Technique
  40. An Effective Approach for Image Denoising Using Wavelet Transform Involving Deep Learning Techniques
  41. Medical Image Analysis using Distributed Deep Learning Models
  42. Efficient Net-Based Deep Learning Approach for Breast Cancer Detection with Mammography Images
  43. Ultra-Short Term Wind Power Probability Prediction Based on Deep Learning and Sparse Gaussian Process of Composite Kernel Function
  44. Speech Emotion Recognition Using Gammatone Cepstral Coefficients and Deep Learning Features
  45. Ischemic Heart Disease Prediction Using Machine Learning and Deep Learning Techniques
  46. People Counting in Public Spaces using Deep Learning-based Object Detection and Tracking Techniques
  47. Deep Learning for Music: A Systematic Literature Review
  48. Deep Learning and Grad-CAM for the Diagnosis of Pneumonia Based on X-Rays
  49. Transfer-Based Deep Learning Technique for PCOS Detection Using Ultrasound Images
  50. Multi-Task and Few-Shot Learning-Based Fully Automatic Deep Learning Platform for Mobile Diagnosis of Skin Diseases