Effect of denoising on performance of deep convolutional neural network for mammogram images classification

H Mechria, K Hassine, MS Gouider - Procedia Computer Science, 2022 - Elsevier
Digital mammograms are an important imaging modality for breast cancer screening and
diagnosis. Several types of noise appear in mammograms and make the job of detecting …

A proposed 3-stage CNN classification model based on augmentation and denoising

MA Joodi, MH Saleh, DJ Kadhim - International Journal of …, 2023 - ijnaa.semnan.ac.ir
This work proposed a CNN classification model that aims to classify the faces by three
stages applied to a real data set. The first stage shows the effects of the augmentation …

Algorithms for human body segmentation and skeleton fusion

K Ryselis - 2023 - epubl.ktu.edu
Abstract [eng] The dissertation presents three algorithms that solve the problems of the
dissertation. The first algorithm, Agrast-6 neural network, automatically segments depth …

[PDF][PDF] An efficient method for high-density multimodal salt-and-pepper noise removal of MRI images

J Ebrahimnejad, A Naghsh - 2022 - scholar.archive.org
Medical image noise reduction is a signi cant and challenging area in image processing. A
new adaptive window-based solution for the removal of high-density multimodal salt-and …

Detection of Malignancy in Mammogram by Modified Convolution Neural Network

AC Vikramathithan, TG Manjunath… - 2021 5th …, 2021 - ieeexplore.ieee.org
In recent decades, cases on breast cancer are increasing rapidly and there are many
awareness campaigns conducted in order to ensure the timely diagnosis of the same …

Identification of Diabetic Retinopathy (DR) using Image Processing

AC Vikramathithan, P Pooja, MS Bhaskar… - Journal of Physics …, 2022 - iopscience.iop.org
Diabetes appears in two varieties: Type-1 and Type-2. The former is chronic and can last for
years together, whereas the latter can be cured if identified and treated at a premature stage …