An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

Raziani, Saeid; Ahmadian, Sajad; Jalali, Seyed Mohammad Jafar; Chalechale, Abdolah

doi:10.1007/s42235-022-00216-x

Cited by 11 publications

(4 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although this method can quickly extract the required features, these features may not have the best recognition effect. Because these feature extraction rules are all derived from experience, when faced with different background environments, the extracted features are good or bad [7][8]. Moreover, there is no unified strategy for how to combine these features to improve the IR effect.…”

Section: Comparison Of Image Recognition (Ir) Methodsmentioning

confidence: 99%

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

2020

View full text Add to dashboard Cite

The current research on automatic recognition of vegetable and fruit images is more focused on a single fruit and vegetable image without background environment, and more features such as texture and color are extracted, and shallow learning technology is used to realize the recognition of vegetable and fruit images. The method cannot meet the classification and identification of various fruits and vegetables. The main purpose of this paper is to carry out research on fruit and vegetable identification and nutritional analysis based on the knowledge of multilayer perceptron and neural network (NN). In view of the above background, this research focuses on the research on IR of fruits and vegetables, using deep learning methods to improve the recognition rate of fruits and vegetables images, and constructing models through CNNs to achieve classification and recognition. Experiments show that the number of network layers has a greater impact on IR, and the deeper the network depth, the higher the IR rate. Considering that the increase in the number of network layers will affect the structural complexity of the network model and increase the amount of mathematical operations, reasonable network layer selection is particularly important in IR.

show abstract

Section: Comparison Of Image Recognition (Ir) Methodsmentioning

confidence: 99%

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

2020

View full text Add to dashboard Cite

show abstract

“…An application of GA hybridization and back propagation showed a better result in breast cancer classification by AlShourbaji et al [48] compared to BP alone. In order to enhance the accuracy of MLPs, the authors of [49] proved that the differential evolution algorithm optimised by metaheuristics still shows better results in the classification of medical data.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid chaotic whale-shark optimization algorithm to improve artificial neural network: application to the skin neglected tropical diseases diagnosis

2023

IJACR

View full text Add to dashboard Cite

The term "neglected tropical diseases" (NTDs) refers to a collection of infectious illnesses linked to poverty as they occur in remote and poor areas of the tropics and mainly affect poor and "voiceless" populations. However, a large number of NTDs are cutaneous and endemic in most regions, creating an opportunity for integrated disease control. A study in Cameroon examined 1,352 children aged 15 years or younger, 54% of whom were male. 751 (55.6%) of these children had at least one Buruli ulcer (BU) skin lesion, 480 (36%) had a yaws lesion, and 271 (20%) had another type of lesion [1]. *Author for correspondenceThe direct consequences for these children are twofold. Firstly, the prolonged morbidity often leads to a long interruption or/and abandonment of going to school. Secondly, amputations or contractures become common, this can disable kids which will not be able to work in the fields. Once they become adults, they will be a burden to society.In view of the increasing number of cases in recent years and in order to alleviate this situation, endangered countries should step up their efforts for the early detection and treatment of instances. This requires early diagnosis of these diseases [3]. Diagnosis of these infections can be difficult in rural areas because pathological anatomy (PA), the search for acid-fast bacilli (AFB) on smears and Ziehl's stain, culture, PCR (polymerase chain reaction), and

show abstract

“…To sum up, the basis of the NNM inference on the side is the lightweight of the model, and the lightweight of the model depends on its own small amount of parameters and calculations. Therefore, the focus of this paper is to design the model around the two core tasks of reducing the complexity of the model's convolution kernel parameters and the calculation amount of participating in the NNM for image recognition, analyzing the main problems faced by the lightweight model, and designing an effective solution to solve [15,16].…”

Section: Analysis Of Research Significancementioning

confidence: 99%

Classification Algorithm for Multiple Image Formats Suorting Complex Neural Network Models

2021

View full text Add to dashboard Cite

In recent years, with the continuous exploration of NNs and the upgrading of mobile phone hardware, more and more researches focus on how to design effective models to suort terminal task reasoning. IC is a classic task in many studies. It can be used in many fields such as image search, face recognition, and medical imaging. It has great practical significance in reality. The main purpose of this paper is to carry out optimization research on the classification algorithm of multiple image formats based on the suort of complex NN models (NNM). This paper proposes improved methods for problem transformation and model adaptation, and studies the problems of blurred scenes and difficult method selection in multi-format image classification (IC). In the direction of problem transformation, a basic classification model and a classification model combined with advanced strategies are designed., in the direction of model adaptation, transform multi-format output and use transfer learning for training. Finally, it is found that the problem transformation method can obtain better classification results in small data sets, but the classification time is several times as long as the model adaptation method. The model adaptation method can save a lot of time and is easier to promote.

show abstract

An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

Cited by 11 publications

References 72 publications

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

Fruit and Vegetable Recognition and Nutrition Analysis Based on Multilayer Perceptron and Neural Network

Hybrid chaotic whale-shark optimization algorithm to improve artificial neural network: application to the skin neglected tropical diseases diagnosis

Classification Algorithm for Multiple Image Formats Suorting Complex Neural Network Models

Contact Info

Product

Resources

About