SKIN DISEASE DETECTION USING CNN (CONVOLUTIONAL NEURAL NETWORK)
Objective
Skin diseases present significant global health challenges. This study employs Convolutional Neural Networks (CNNs) for automated skin disease detection, utilizing diverse lesion images to train and evaluate the model's performance.
Abstract
Skin diseases are prevalent health concerns globally, necessitating efficient and accurate diagnostic approaches. Convolutional Neural Networks (CNNs) have emerged as powerful tools in medical image analysis, offering potential for automated disease detection. This study focuses on utilizing CNNs for skin disease detection. By leveraging a dataset comprising diverse skin lesion images, a CNN model is trained to classify images into various disease categories. The proposed method involves preprocessing input images, designing CNN architecture, and training the model on annotated data. Evaluation is conducted using metrics such as accuracy, precision, recall, and F1 score, ensuring robust performance assessment. The results showcase the efficacy of CNNs in accurately identifying skin diseases, thereby aiding in timely diagnosis and treatment planning. This research contributes to advancing computer-aided diagnostic systems, offering a promising avenue for enhancing healthcare delivery in dermatology.
Keywords: Skin disease detection, Convolutional Neural Network, CNN, Medical image analysis, Diagnosis, Healthcare.
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
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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.
