ADAPTIVE FLAME BASED SEGMENTATION AND CLASSIFICATION FOR BONE CANCER DETECTION
Objective
Bone cancer, a rare malignancy, necessitates timely detection for improved outcomes. AFLAME, an adaptive fuzzy clustering method, introduces a strategy for identifying bone cancer. SVM classifiers aid classification, promising enhanced diagnosis and treatment planning.
Abstract
Bone cancer, or bone sarcoma, is a rare malignancy characterized by the abnormal growth of tissue within bones, often with a high propensity for metastasis. Timely classification and detection are critical for improving patient outcomes. This study introduces an adaptive fuzzy clustering method called AFLAME to explore a potential strategy for identifying bone cancer. Accurate classification and segmentation of bone tumors are crucial for various applications, yet achieving this has been challenging due to limitations in existing methods, such as insufficient non-homogeneous and contrast intensity in medical imaging techniques. Support vector machine (SVM) classifiers are employed to facilitate the classification process. The proposed method offers a new approach to segmenting bone cancer, paving the way for further investigations in this vital area. Efforts in this direction hold promise for enhancing early diagnosis and treatment planning, ultimately improving the prognosis for individuals affected by bone cancer.
Keywords: bone cancer, classification, segmentation, fuzzy clustering, local approximation.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is an Image/pixel?
· About image formats
· Introduction to Image Processing
· How digital image is formed
· Importing the image via image acquisition tools
· Analyzing and manipulation of image.
· Phases of image processing:
o Acquisition
o Image enhancement
o Image restoration
o Color image processing
o Image compression
o Morphological processing
o Segmentation etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
Request Call Back
Would you like to receive a free callback now?
Choose the best time for callback:
Leave your message and we will contact you as soon as possible
6-2-85/B, Old Maternity Hospital Road, Thyagaraja Nagar, Tirupati, Andhra Pradesh – 517501
+91 9030333433
+91 9393939065
0877-2261612
Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
