Affiliations: [a] Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India
| [b] Department of ECE, Mahendra Institute of Technology, Namakkal, Tamilnadu, India
| [c] Department of ECE, Sona College of Technology, Salem, Tamilnadu, India
| [d] Department of Electricaland Electronics Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamilnadu, India
Correspondence:
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Corresponding author. Bhavithra Janakiraman, Assistant Professor, Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India. E-mail: [email protected].
Abstract: Having one’s life threatened by a disease like ovarian cancer is the single most crucial thing in the whole world. It is difficult to achieve high performance without sacrificing computational efficiency; the results of the denoising process are not as good as they could be; the proposed models are nonconvex and involve several manually chosen parameters, which provides some leeway to boost denoising performance; the methods generally involve a complex optimisation problem in the testing stage; Here at DnCNN, we’ve developed our own version of the deep ii learning model, a discriminative learning technique. The goal was to eliminate the need for the iterative optimisation technique at the time it was being evaluated. The goal was to avoid having to go through testing altogether, thus this was done. It is highly advised to use a Deep CNN model, the efficacy of which can be evaluated by comparing it to that of more traditional filters and pre-trained DnCNN. The Deep CNN strategy has been shown to be the best solution to minimise noise when an image is destroyed by Gaussian or speckle noise with known or unknown noise levels. This is because Deep CNN uses convolutional neural networks, which are trained using data. This is because convolutional neural networks, which are the foundation of Deep CNN, are designed to learn from data and then use that learning to make predictions. Deep CNN achieves a 98.45% accuracy rate during testing, with an error rate of just 0.002%.
Keywords: Ovarian follicles, cancer, deep learning, hybrid optimization, noise levels, magnetic resonance imaging
