An Optimized Deep Convolutional Neural Network Architecture for Concept Drifted Image Classification

Jameel, Syed Muslim; Hashmani, Manzoor Ahmed; Alhussain, Hitham; Rehman, Mobashar; Budiman, Arif

doi:10.1007/978-3-030-29516-5_70

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Remarkably, the proposed approach (ensemble approach) contributes to diversity in a simple yet effective manner. This study also used the single-instance optimized CNN model inspired by [34,35] (which was carefully devised after numerous experiments) as an instance in the cloud server's ensemble. Furthermore, the authors trained the proposed model using a challenging dataset (the ISIC 2019 dataset).…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

et al. 2021

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The prevalence of skin diseases has increased dramatically in recent decades, and they are now considered major chronic diseases globally. People suffer from a broad spectrum of skin diseases, whereas skin tumors are potentially aggressive and life-threatening. However, the severity of skin tumors can be managed (by treatment) if diagnosed early. Health practitioners usually apply manual or computer vision-based tools for skin tumor diagnosis, which may cause misinterpretation of the disease and lead to a longer analysis time. However, cutting-edge technologies such as deep learning using the federated machine learning approach have enabled health practitioners (dermatologists) in diagnosing the type and severity level of skin diseases. Therefore, this study proposes an adaptive federated machine learning-based skin disease model (using an adaptive ensemble convolutional neural network as the core classifier) in a step toward an intelligent dermoscopy device for dermatologists. The proposed federated machine learning-based architecture consists of intelligent local edges (dermoscopy) and a global point (server). The proposed architecture can diagnose the type of disease and continuously improve its accuracy. Experiments were carried out in a simulated environment using the International Skin Imaging Collaboration (ISIC) 2019 dataset (dermoscopy images) to test and validate the model’s classification accuracy and adaptability. In the future, this study may lead to the development of a federated machine learning-based (hardware) dermoscopy device to assist dermatologists in skin tumor diagnosis.

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Section: Methodsmentioning

confidence: 99%

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

et al. 2021

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“…The useful trends will allow us to make nontrivial predictions about new data and are insightful in many significant ways about the already-observed data. However, to make matters worse, it is not always simple to identify these trends [26] [27].To succeed, we will have to use more sophisticated means of representing these systemic trends in results.…”

Section: Data Mining Implications In the Healthcare Sectormentioning

confidence: 99%

Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

Jamil

2022

IJCAI

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Many researchers are trying hard to minimize the incidence of cancers, mainly GC. For GC, the five-year survival rate is generally 5-25%, but for EGC, it is almost up to 90%. Among the cancers, GC is very deadly. It is difficult for doctors to assess its threat to patients as it requires years of medical practice and rigorous testing. The healthcare sector has benefitted from AI for the early diagnosis or classification of GC. However, the current AI-based techniques need further improvement in clinical testing. Heterogeneous GC characterization requires more optimized methods for early detection of GC because of its type and severity. Hence, it is essential to investigate this area further and develop more optimized approaches for early diagnosis. Early detection will increase the chances of successful treatments. In this study, we have conducted a literature survey detailing the role of AI in the healthcare sector for GC diagnosis. We discuss basic principles, advantages and disadvantages, training and testing of data, and integration of applications like DSS, CDSS, KDD, ML, DM, BD, and DL, and their relevance to the healthcare industry. The study focuses on the application of ML techniques used in the diagnosis of GC. This review paper also introduces DM techniques, their application in the healthcare industry, limitations, roles, and operational challenges. These assist pathologists in helping minimize their workload while increasing diagnostic accuracy. These techniques will further assist medical practitioners with their decision-making process. Povzetek: Raziskava o uporabi tehnik strojnega učenja pri diagnostiki raka želodca v zdravstvenem sektorju.

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“…The general customization of automated image processing is an open issue in the scientific community of pattern analysis [83,84], and the machine learning approaches used must take into account differences in the acquisition platform, the specific location, the hardware imaging settings, and, most importantly, the wide differences of the targeted species [85]. Firstly, the huge variety of marine fauna shapes and sizes are the most relevant factors affecting the performance of shape-centered recognition and classification.…”

Section: Automated Video-imagingmentioning

confidence: 99%

The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms

Aguzzi

Chatzievangelou

Francescangeli

et al. 2020

Sensors

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Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.

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An Optimized Deep Convolutional Neural Network Architecture for Concept Drifted Image Classification

Cited by 5 publications

References 8 publications

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

An Adaptive Federated Machine Learning-Based Intelligent System for Skin Disease Detection: A Step toward an Intelligent Dermoscopy Device

Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

The Hierarchic Treatment of Marine Ecological Information from Spatial Networks of Benthic Platforms

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