A hybrid bat-genetic algorithm for improving the visual quality of medical images

Hussin, Kholood N.; Nahar, Ali Kareem; Khleaf, Hussain Kareem

doi:10.11591/ijeecs.v28.i1.pp220-226

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…One optimization technique, GA, allows searching inside very large, complex areas [32]. GA is a method of randomized search [33]. This algorithm effectively adapts to the environment (both known and unknown); it is used in the 2D path planning of a humanoid robot and the 3D path planning problem for aerial and underwater vehicles [20].…”

Section: Heuristic Approachesmentioning

confidence: 99%

Navigation and Path Planning of Mobile Robots

Hassan,

Abed,

Al-Hussaibi

2023

JESA

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Autonomous mobile robots grapple with the complexities of navigating their operational environment and exercising independent decision-making. A principal challenge in robotics lies in path planning, i.e., identifying the optimal route from an origin to a destination. Various approaches to surmount this challenge have been probed by researchers, taking into account parameters such as the environment, the type of robot, and application prerequisites. A proficient path-planning algorithm stands as a linchpin for secure mobile robot navigation and the triumphant execution of robotics applications. Typically, the primary objective of the navigation process is to minimize the distance traversed, given its implications on other metrics such as processing time and energy consumption. This research aims to shed light on the pivotal components of mobile robot environment representation, navigation, and offers an analysis of certain path-planning techniques. When the development of navigation algorithms initially kicked off, classical techniques such as Artificial Potential Field (APF), Cell Decomposition, and Roadmap gained popularity. However, compared to their predecessors, heuristic path planning techniques like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) have recently witnessed a surge in exploration. This research presents a balanced examination of the merits and demerits of these methods.

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Section: Heuristic Approachesmentioning

confidence: 99%

Navigation and Path Planning of Mobile Robots

Hassan,

Abed,

Al-Hussaibi

2023

JESA

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“…The linear regression algorithm which calculates the value of a dependent variable, establishes an independent variable [18]. The hybrid bat algorithm and genetic algorithm model are employed on noisy medical images to reduce noise, and their performances have been determined by arithmetical analyses such as peak signal-to-noise ratio (PSNR) [19]. DL is employed to classify three focal diseases in the liver besides the normal liver [20].…”

Section: Introductionmentioning

confidence: 99%

Design of inception architecture for skin melanoma classification

Manikandan

Karthikeyan

2023

IJEECS

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The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods of examination may be used to investigate it. However, the invasive method is more difficult for the patient because samples must be taken from the lesion itself, or the whole lesion must be cut out. It also requires more time and cost. To avoid invasive procedures, computer-based analysis and diagnosis have the potential to increase diagnostic accuracy and turnaround time. This study develops a unique discriminative deep learning architecture (DDLA) for dermoscopic image classification (DIC), called DDLA-DIC, which uses the concept of inception. Using this concept, the proposed DDLA-DIC system is designed wider and deeper and the network learns from various spatial patterns. The proposed DDLA-DIC system can extract image characteristics from dermoscopic images for skin cancer diagnosis in an effective and efficient way. The proposed DDLA-DIC system is evaluated by utilizing the dermoscopic images from the PH2 database, and the obtained classification results are based on a random split approach. The simulation results indicate that the framework has a great deal of potential with 99.79% accuracy.

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A hybrid bat-genetic algorithm for improving the visual quality of medical images

Cited by 2 publications

References 16 publications

Navigation and Path Planning of Mobile Robots

Navigation and Path Planning of Mobile Robots

Design of inception architecture for skin melanoma classification

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