Implementation of noise and hair removals from dermoscopy images using hybrid Gaussian filter

Nyemeesha, V.; Ismail, B. Mohammed

doi:10.1007/s13721-021-00318-2

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…An accurate automated computer diagnosed health care system is essential in detection of deadliest disease that causes death to humans in early stage. Deeplearning-based [26] technologies have recently acquired appeal in medicine because to their power and flexibility in utilizing accessible dataset. A Deep-CNN is a multi layered deep learning technique for extracting features from data automatically.…”

Section: Methodsmentioning

confidence: 99%

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Sripada

2022

ijhs

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Melanoma is a malignant skin disease that kills roughly one million people per year, as per WHO records. The proposed work's major goal is to create and develop an enhanced deep neural network model that can evaluate, recognize, and predict cutaneous skin disease in its early stages with more accuracy, lowering the chance of death. The dataset, which was gathered from dermatologists and the public domain, contains roughly 3606 images of various types of skin lesions; some of the images were distorted. Gaussian and bilateral filters were used to decrease the noise in the images. After cleaning the data, the proposed six Convolutional layered Deep-CNN networks deployed to identify and predict skin cancer. The proposed CNN network was able to recognize skin diseases such Malignant Melanoma, Squamous cellcarcinoma, and Basal cellcarcinoma with an optimal error rate and good accuracy of 98.07 percent.

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Section: Methodsmentioning

confidence: 99%

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

Sripada

2022

ijhs

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“…These tactics were effective for small datasets; however, they were ineffective for the ISIC challenge datasets. In order to categorize skin lesions, artificial neural networks (ANN) such as Backpropagated ANN [41], DenseNet-201 [42], CNN with data augmentation (CNN-DG) [43], DLCNN [44], and MCM-CNN [45] have been used. In [46], the authors used Ensemble hybrid CNN(HECNN) for multi-class skin lesion classification, and they found it to be effective.…”

Section: Literature Surveymentioning

confidence: 99%

“…The proposed hybrid feature extraction uses GLCM, and DWT modules to solve these issues. [43] and CNN [44] techniques. All tests are done using ISIC-2019 dataset.…”

Section: Segmentation Analysismentioning

confidence: 99%

SCDC-Net: Skin Cancer Detection and Classification using Deep Q Neural Network with Hybrid Features

Nyemeesha

2022

IJRITCC

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Skin cancer is one of the most severe malignancies that may affect individuals of all ages. It can affect people of all races and ethnicities. Thus, early detection and treatment of skin cancer have the potential to save millions of lives. Because of the high degree of similarity between skin lesions, conventional machine learning approaches failed to provide the highest possible detection and classification accuracy in the skin cancer detection and classification (SCDC) system. To achieve the robust performance, this work is presented a SCDC-Net by employing deep learning, transfer learning models with hybrid features. Initially, UG-Net based transfer learning model is developed by combining generative adversarial networks (GAN) and U-Net. Here, UG-Net is used to remove hair from skin lesions and also preprocess enhances lesion. In addition, Hybrid U-Net (HU-Net) is used to localize the disease effected area of skin cancer. Further, various hybrid features are extracted from the segmented output by employing the gray-level cooccurrence matrix (GLCM) based texture characteristics, the discrete wavelet transform (DWT)-based low-level features, and the computation of image statistics for the texture, low-level, and colour features, respectively. In order to ensure the highest possible performance of the system, a deep q neural network (DQNN) was constructed for the classification of skin cancer using GLCM, DWT, and statistical colour features. The simulation results shows that the proposed method resulted in superior subjective and objective performance as compared to state of art approaches on ISIC-2019 public challenge dataset.

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“…The evolution of SBIR has closely paralleled advancements in image processing [5,6], computer vision, and machine learning. Originating in the late 1990s as an offshoot of CBIR, early SBIR systems focused on basic shape matching using simple feature extraction techniques [7].…”

Section: Introductionmentioning

confidence: 99%

Zero-Shot Sketch-Based Image Retrieval Using StyleGen and Stacked Siamese Neural Networks

Gopu,

Dunna

2024

J. Imaging

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Sketch-based image retrieval (SBIR) refers to a sub-class of content-based image retrieval problems where the input queries are ambiguous sketches and the retrieval repository is a database of natural images. In the zero-shot setup of SBIR, the query sketches are drawn from classes that do not match any of those that were used in model building. The SBIR task is extremely challenging as it is a cross-domain retrieval problem, unlike content-based image retrieval problems because sketches and images have a huge domain gap. In this work, we propose an elegant retrieval methodology, StyleGen, for generating fake candidate images that match the domain of the repository images, thus reducing the domain gap for retrieval tasks. The retrieval methodology makes use of a two-stage neural network architecture known as the stacked Siamese network, which is known to provide outstanding retrieval performance without losing the generalizability of the approach. Experimental studies on the image sketch datasets TU-Berlin Extended and Sketchy Extended, evaluated using the mean average precision (mAP) metric, demonstrate a marked performance improvement compared to the current state-of-the-art approaches in the domain.

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Implementation of noise and hair removals from dermoscopy images using hybrid Gaussian filter

Cited by 8 publications

References 16 publications

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

novel approach to classify skin malignancy through deep convolutional neural network and image preprocessing approaches

SCDC-Net: Skin Cancer Detection and Classification using Deep Q Neural Network with Hybrid Features

Zero-Shot Sketch-Based Image Retrieval Using StyleGen and Stacked Siamese Neural Networks

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