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Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images

Escorcia-Gutierrez, Jose; Mansour, Romany F.; Beleño, Kelvin; Jiménez-Cabas, Javier; Pérez, Meglys; Madera, Natasha; Velasquez, Kevin (Tech Science PressUnited States, 2022)

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of ...
Deep learning with backtracking search optimization based skin lesion diagnosis model

Anupama, C. S. S.; Natrayan, L.; Laxmi Lydia, E.; Wahab Sait, Abdul Rahaman; Escorcia-Gutierrez, Jose; Gamarra, Margarita; Mansour, Romany F. (Tech Science PressUnited States, 2021-09-07)

Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized ...
A feature selection strategy to optimize retinal vasculature segmentation

Escorcia-Gutierrez, Jose; Torrents-Barrena, Jordina; Gamarra, Margarita; Madera, Natasha; Romero-Aroca, Pedro; Valls, Aida; Puig, Domenec (Tech Science PressUnited States, 2022)

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings ...
Improved metaheuristics with machine learning enabled medical decision support system

Althubiti , Sara ; Escorcia-Gutierrez, Jose; Gamarra, Margarita; Soto-Diaz, Roosvel; Mansour, Romany F.; Alenezi, Fayadh (Tech Science Press, 2022)

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence ...
Optimal synergic deep learning for COVID-19 classification using chest x-ray images

Escorcia-Gutierrez, José; Gamarra, Margarita; Soto-Diaz, Roosvel; alsafari, safa; Yafoz, Ayman; Mansour, Romany F. (Tech Science PressUnited States, 2023-04-29)

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging ...

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Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images
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Escorcia-Gutierrez, Jose | 2022

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.

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Deep learning with backtracking search optimization based skin lesion diagnosis model
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Anupama, C. S. S. | 2021-09-07

Nowadays, quality improvement and increased accessibility to patient data, at a reasonable cost, are highly challenging tasks in healthcare sector. Internet of Things (IoT) and Cloud Computing (CC) architectures are utilized in the development of smart healthcare systems. These entities can support real-time applications by exploiting massive volumes of data, produced by wearable sensor devices. The advent of evolutionary computation algorithms and Deep Learning (DL) models has gained significant attention in healthcare diagnosis, especially in decision making process. Skin cancer is the deadliest disease which affects people across the globe. Automatic skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin lesions. The current research article presents a new skin lesion diagnosis model i.e., Deep Learning with Evolutionary Algorithm based Image Segmentation (DL-EAIS) for IoT and cloud-based smart healthcare environments. Primarily, the dermoscopic images are captured using IoT devices, which are then transmitted to cloud servers for further diagnosis. Besides, Backtracking Search optimization Algorithm (BSA) with Entropy-Based Thresholding (EBT) i.e., BSA-EBT technique is applied in image segmentation. Followed by, Shallow Convolutional Neural Network (SCNN) model is utilized as a feature extractor. In addition, Deep-Kernel Extreme Learning Machine (D-KELM) model is employed as a classification model to determine the class labels of dermoscopic images. An extensive set of simulations was conducted to validate the performance of the presented method using benchmark dataset. The experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

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A feature selection strategy to optimize retinal vasculature segmentation
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Escorcia-Gutierrez, Jose | 2022

Diabetic retinopathy (DR) is a complication of diabetes mellitus that appears in the retina. Clinitians use retina images to detect DR pathological signs related to the occlusion of tiny blood vessels. Such occlusion brings a degenerative cycle between the breaking off and the new generation of thinner and weaker blood vessels. This research aims to develop a suitable retinal vasculature segmentation method for improving retinal screening procedures by means of computer-aided diagnosis systems. The blood vessel segmentation methodology relies on an effective feature selection based on Sequential Forward Selection, using the error rate of a decision tree classifier in the evaluation function. Subsequently, the classification process is performed by three alternative approaches: artificial neural networks, decision trees and support vector machines. The proposed methodology is validated on three publicly accessible datasets and a private one provided by Hospital Sant Joan of Reus. In all cases we obtain an average accuracy above 96% with a sensitivity of 72% in the blood vessel segmentation process. Compared with the state-of-the-art, our approach achieves the same performance as other methods that need more computational power. Our method significantly reduces the number of features used in the segmentation process from 20 to 5 dimensions. The implementation of the three classifiers confirmed that the five selected features have a good effectiveness, independently of the classification algorithm.

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Improved metaheuristics with machine learning enabled medical decision support system
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Althubiti , Sara | 2022

Smart healthcare has become a hot research topic due to the contemporary developments of Internet of Things (IoT), sensor technologies, cloud computing, and others. Besides, the latest advances of Artificial Intelligence (AI) tools find helpful for decision-making in innovative healthcare to diagnose several diseases. Ovarian Cancer (OC) is a kind of cancer that affects women’s ovaries, and it is tedious to identify OC at the primary stages with a high mortality rate. The OC data produced by the Internet of Medical Things (IoMT) devices can be utilized to differentiate OC. In this aspect, this paper introduces a new quantum black widow optimization with a machine learning-enabled decision support system (QBWO-MLDSS) for smart healthcare. The primary intention of the QBWO-MLDSS technique is to detect and categorize the OC rapidly and accurately. Besides, the QBWO-MLDSS model involves a Z-score normalization approach to pre-process the data. In addition, the QBWO-MLDSS technique derives a QBWO algorithm as a feature selection to derive optimum feature subsets. Moreover, symbiotic organisms search (SOS) with extreme learning machine (ELM) model is applied as a classifier for the detection and classification of ELM model, thereby improving the overall classification performance. The design of QBWO and SOS for OC detection and classification in the smart healthcare environment shows the study’s novelty. The experimental result analysis of the QBWO-MLDSS model is conducted using a benchmark dataset, and the comparative results reported the enhanced outcomes of the QBWO-MLDSS model over the recent approaches.

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Optimal synergic deep learning for COVID-19 classification using chest x-ray images
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Escorcia-Gutierrez, José | 2023-04-29

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the lungs. Chest X-ray (CXR) gained much interest after the COVID-19 outbreak thanks to its rapid imaging time, widespread availability, low cost, and portability. In radiological investigations, computer-aided diagnostic tools are implemented to reduce intra- and inter-observer variability. Using lately industrialized Artificial Intelligence (AI) algorithms and radiological techniques to diagnose and classify disease is advantageous. The current study develops an automatic identification and classification model for CXR pictures using Gaussian Filtering based Optimized Synergic Deep Learning using Remora Optimization Algorithm (GF-OSDL-ROA). This method is inclusive of preprocessing and classification based on optimization. The data is preprocessed using Gaussian filtering (GF) to remove any extraneous noise from the image’s edges. Then, the OSDL model is applied to classify the CXRs under different severity levels based on CXR data. The learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the work. OSDL model, applied in this study, was validated using the COVID-19 dataset. The experiments were conducted upon the proposed OSDL model, which achieved a classification accuracy of 99.83%, while the current Convolutional Neural Network achieved less classification accuracy, i.e., 98.14%.

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Listar por eissn "1546-2226"

Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images ﻿

Deep learning with backtracking search optimization based skin lesion diagnosis model ﻿

A feature selection strategy to optimize retinal vasculature segmentation ﻿

Improved metaheuristics with machine learning enabled medical decision support system ﻿

Optimal synergic deep learning for COVID-19 classification using chest x-ray images ﻿

Envíos recientes

Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images

Deep learning with backtracking search optimization based skin lesion diagnosis model

A feature selection strategy to optimize retinal vasculature segmentation

Improved metaheuristics with machine learning enabled medical decision support system

Optimal synergic deep learning for COVID-19 classification using chest x-ray images

Automated deep learning empowered breast cancer diagnosis using biomedical mammogram images

Deep learning with backtracking search optimization based skin lesion diagnosis model

A feature selection strategy to optimize retinal vasculature segmentation

Improved metaheuristics with machine learning enabled medical decision support system

Optimal synergic deep learning for COVID-19 classification using chest x-ray images