Skin Lesion Segmentation by using Deep Learning Techniques

Hasan, Sohaib Najat; Gezer, Murat; Azeez, Raghad Abdulaali; Gülseçen, Sevinç

doi:10.1109/tiptekno.2019.8895078

Cited by 24 publications

(10 citation statements)

References 12 publications

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“…These are complicated and need planning and perception to classify objects used by robotics for many application s like selfdrivingcare and remote scene classification for military purposes. Semantic Segmentation provides informat ion about the scene objects' components, classified into 8 categories: construction, flat, sky, human, object, void, vehicle, and nature [3]. The proposed algorithm for the proposed system contains many stages, as shown in the following diagram figure ( 1 ).…”

Section: Methodsmentioning

confidence: 99%

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Street Scene understanding via Semantic Segmentation Using Deep Learning

Noori¹,

Shaker²,

Azeez³

2022

ETJ

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 Scene classification is an essential conception task used by robotics for understanding the environment.  The deep learning technique has been proved as a great role in the challenging scene understanding application.  Using data augmentation to increase dataset size  Using K-means clustering as a preprocessor for the input dataset  The proposed hydride model is generated by combined two of the deep , deep neural networks as an xception and U-net models.Scene classification is an essential conception task used by robotics for understanding the environment. Like the street scene, the outdoor scene is composed of images with depth that has a greater variety than iconic object images. Image semantic segmentation is an important task for Autonomous driving and M obile robotics applications because it introduces enormous information needed for safe navigation and complex reasoning. This paper provides a model for semantic segmentation of outdoor sense to classify each object in the scene. The proposed network model generates a hybrid model that combines U-NET with Xception networks to work on 2.5 dimensions cityscape dataset, which is used for 3D applications. This process contains two stages. The first is the pre-processing operation on the RGB-D dataset (data Augmentation and k-means cluster). The second stage designed the hybrid model, which achieves a pixel accuracy is 0.7874. The output module is generated using a computer with GPU memory NVIDIA GeForce RTX 2060 6G, programming with python 3.7.

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Section: Methodsmentioning

confidence: 99%

“…This paper works on cityscape with depth dataset, which has many weakly labeled images. The challenge with working with 3D or 2.5D datasets is that they are not many accurate and highresolution datasets in that field [2,3].…”

Section: Introductionmentioning

confidence: 99%

Street Scene understanding via Semantic Segmentation Using Deep Learning

Noori¹,

Shaker²,

Azeez³

2022

ETJ

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“…The researchers described the investigation of the relevancy of deep learning by utilizing a pre-trained VGG16 encoder and combined it with DeeplabV3, SegNet decoder, and TernausNet [ 83 ]. A deep learning strategy to perform and refine the important task of skin lesion segmentation is utilized by 46 layered U-net frameworks and a modified U-Net framework to achieve a successful lesion segmentation rate [ 84 ]. To resolve the challenges of varying size and the appearance of skin lesion segmentation, a new dense deconvolutional framework is designed.…”

Section: Skin Cancer Recognition and Classification Systemmentioning

confidence: 99%

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Zafar

Sharif

et al. 2023

Life

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The skin is the human body’s largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.

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“…The threshold method is an effective way to segment an image. This method is based on statistical analysis and it depends on peak spatial details which are found in the face [10, 25,26]. A digital image is classified according to the Intensity Level (INT), this operation converts the image into (X×Y) pixels, each pixel represents an INT value that belongs to the level which is classified if the image is a grey level image.…”

Section: Image Segmentation Techniquesmentioning

confidence: 99%

Multibiometric System with Runs Bits Permutation for Creating Cryptographic key Generation Technique

Jamil

Azeez

Al-Adhami

et al. 2023

Iraqi Journal of Science

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The context of multibiometric plays a pivotal role in enhancing an identification system, since a biometric system is now the most physical way of identifying and verifying individuals. The feature of multibiometric could be merged to produce identification information. However, unimodal biometric systems suffer from different types of breaching. Thus, mixing biometrics with cryptography leads to overcome small variations existing between diverse acquisitions of the same biometric in order to produce the robust system. In this paper, a new robust multibiometric system is proposed to create a random key from person multibiometric, facial and fingerprint images which are used simultaneously to produce this key. Several manipulations are made on compactness information for these two images to get a unique key for each person. The generated random key can be used for electronic numbers, passport identification, civil identification card, and it could be used as seeds for pseudo-random number generators. The multi-biometric system operates on two images, faces and fingerprints, by partitioning each image into four parts and taking the highest density for each one, XOR these parts; diffusions process is applied on these parts including permutation and thresholding to produce a random key. The generated key cannot be revocable that passed through randomness tests to ensure whether the generated key is accepted as true. Thus, the results of the tests are passed and presented that all generated keys are accepted to be random and unpredictable binary sequences and hence they can be used efficiently.

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Skin Lesion Segmentation by using Deep Learning Techniques

Cited by 24 publications

References 12 publications

Street Scene understanding via Semantic Segmentation Using Deep Learning

Street Scene understanding via Semantic Segmentation Using Deep Learning

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Multibiometric System with Runs Bits Permutation for Creating Cryptographic key Generation Technique

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