Images data practices for Semantic Segmentation of Breast Cancer using Deep Neural Network

Ahmed, Luqman; Iqbal, Muhammad Munwar; Aldabbas, Hamza; Khalid, Shehzad; Saleem, Yasir; Saeed, Saqib

doi:10.1007/s12652-020-01680-1

Cited by 88 publications

(51 citation statements)

References 28 publications

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“…L. Ahmed et al [ 35 ], have introduced their breast cancer SS study of images data practices using deep neural network, their study validated by two mammography’s images datasets (Mammographic Image Analysis Society (MIAS), and Curated Breast Imaging Subset of (Digital Database for Screening Mammography) (CBIS-DDSM)), proposing a preprocessing mechanism for removing noise, artifacts and muscle region which could cause a high false positive rate.…”

Section: Introductionmentioning

confidence: 99%

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

et al. 2021

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Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

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Section: Introductionmentioning

confidence: 99%

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

et al. 2021

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“…Most of the web servers and applications widely used protocol in application layer is hypertext transfer protocol (HTTP). The increased HTTP traffic [3] and technological development of the internet invites the attackers to launch the HTTP-based attacks.…”

Section: Flow Based Intrusion Detection System With Whale Optimization and Evolutionary Algorithms (Ea) For Diversified Traffic Streams 1mentioning

confidence: 99%

“…In recent studies, rule based datamining, artificial neural network (ANN) [4] , evolutionary algorithms and swarm intelligence have gained significant importance to address the Application layer DDoS attacks. However, the ANN based methods have two short comings.…”

Section: Flow Based Intrusion Detection System With Whale Optimization and Evolutionary Algorithms (Ea) For Diversified Traffic Streams 1mentioning

confidence: 99%

Flow based intrusion Detection system with Whale Optimization and Evolutionary Algorithms (EA) for Diversified Traffic Streams

Rajiv¹,

Chandra²,

Prashanthi³

et al. 2021

Preprint

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The technological growth and advances in the internet led to the generation of huge volume of data that networks must be capable of transmitting. Providing security to this data is a challenging task. The development in the internet attracts several vulnerable attacks. The researchers in the literature proposed several machine learning, Deep learning and ANN based approaches for efficient attack detection. However, these approaches are prone to high false alarm rates and exhibits poor performance for diversified incoming traffic, because these methodologies relay on the packet level or transaction level features. The performance is inversely proposal to the diversity ratio of packet level features. To handle this, we introduced a combination of high-performed evolutionary algorithms and neural networks for attack classification at flow level with low false alarm rates and high detection accuracy. A unique set of flow features are defined to handle the traffic at flow level and optimal feature selection using whale Optimization Algorithm (WOA). The gravitational search (GS), and particle swarm optimization (PSO) combinations are used in attack detection phase to train the ANN and results proposed model as GSPSO-ANN with WOA. The performance of the proposed model is evaluated with NSL-KDD and CSE-CIC-IDS2018 datasets. The results are compared with other ANN based conventional methods. The results inferred that the proposed GSPSO-ANN with WOA attained maximum detection accuracy with low false alarm rates and processing time and also maintained consistency in the performance for diversified traffic.

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“…In 2014, Google introduced a supervised deep-learning semantic segmentation model called DeepLab [27]. With remarkable advantages, DeepLab has become a hot topic in research and engineering [28][29][30][31][32][33], and one of its popular variants, DeepLab v3 [34], has been widely used in medical image processing [35][36][37][38][39][40][41]. We propose an automatic cone photoreceptor cell identification algorithm based on Dee-pLab v3 for AO-SLO images.…”

Section: Introductionmentioning

confidence: 99%

DeepLab and Bias Field Correction Based Automatic Cone Photoreceptor Cell Identification with Adaptive Optics Scanning Laser Ophthalmoscope Images

Chen

Wang

et al. 2021

Wireless Communications and Mobile Computing

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The identification of cone photoreceptor cells is important for early diagnosing of eye diseases. We proposed automatic deep-learning cone photoreceptor cell identification on adaptive optics scanning laser ophthalmoscope images. The proposed algorithm is based on DeepLab and bias field correction. Considering manual identification as reference, our algorithm is highly effective, achieving precision, recall, and F 1 score of 96.7%, 94.6%, and 95.7%, respectively. To illustrate the performance of our algorithm, we present identification results for images with different cone photoreceptor cell distributions. The experimental results show that our algorithm can achieve accurate photoreceptor cell identification on images of human retinas, which is comparable to manual identification.

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Images data practices for Semantic Segmentation of Breast Cancer using Deep Neural Network

Cited by 88 publications

References 28 publications

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Flow based intrusion Detection system with Whale Optimization and Evolutionary Algorithms (EA) for Diversified Traffic Streams

DeepLab and Bias Field Correction Based Automatic Cone Photoreceptor Cell Identification with Adaptive Optics Scanning Laser Ophthalmoscope Images

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