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Manikandan, R.
- Classification of Cervical Cancer in Women Using Convolutional Neural Network
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Authors
Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science, The Quaide Milleth College for Men, IN
3 Department of Mechanical Engineering, Rathinam Technical Campus, IN
4 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, IN
5 Department of Computer Science, Cork Institute of Technology, IE
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science, The Quaide Milleth College for Men, IN
3 Department of Mechanical Engineering, Rathinam Technical Campus, IN
4 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, IN
5 Department of Computer Science, Cork Institute of Technology, IE
Source
ICTACT Journal on Image and Video Processing, Vol 11, No 4 (2021), Pagination: 2470-2474Abstract
Cervical cancer is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with cervical cancer. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses Convolutional Neural Network (CNN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a pre-processing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over cervical cancer image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models.Keywords
Machine Learning, Cervical Cancer, Classification, Diagnosis.
References
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- IRIS Detection For Biometric Pattern Identification Using Deep Learning
Abstract Views :100 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Presidency University, IN
3 Department of Computer Science, The Quaide Milleth College for Men, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Presidency University, IN
3 Department of Computer Science, The Quaide Milleth College for Men, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 2 (2021), Pagination: 2610-2614Abstract
In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods.Keywords
Iris Detection, Pattern Identification, Liveness Detection, Biometric, Deep LearningReferences
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- Mathematical Morphology based Digital Image Enhancement Processing with Cross Separate Boundary Objects
Abstract Views :106 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology, Kalyani, IN
3 Department of Master of Science in Computing, University of Northampton, GB
1 Department of Computer Science, The Quaide Milleth College for Men, IN
2 Indian Institute of Information Technology, Kalyani, IN
3 Department of Master of Science in Computing, University of Northampton, GB
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 4 (2022), Pagination: 2699-2703Abstract
In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. The Image processing as a subgroup or background of digital signal processing has many advantages over analog image processing. The Digital image processing allows the use of a wide range of algorithms for input data and avoids problems such as noise accumulation and signal distortion during the processing process. Because images are defined in two dimensions (perhaps more than two dimensions), image processing can be formatted into multi-dimensional systems. In this paper an effective Mathematical morphology model was proposed to enhance the quality of images. In this mode, the image is pre-processed and then the gradient is changed using a mathematical image system. Then, the edges are detected by the margin detection method based on the statistical data. This method removes the shadow contours caused by the lights, directly separates the boundaries of the objects and has an impact on the background noise suppression.Keywords
Digital Image Processing, Computer Algorithms, Digital Images, Mathematical MorphologyReferences
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