Automatic Leukaemia Segmentation Approach for Blood Cancer Classification Using Microscopic Images

Some human diseases are recognized through of each type of White Blood Cell (WBC) count, so detecting and classifying each type is important for human healthcare. The main aim of this paper is to propose a computer-aided WBCs utility analysis tool designed, developed, and evaluated to classify WBCs into five types namely neutrophils, eosinophils, lymphocytes, monocytes, and basophils. Using a computer-artificial model reduces resource and time consumption. Various pre-trained deep learning models have been used to extract features, including AlexNet, Visual Geometry Group (VGG), Residual Network (ResNet), which belong to different taxonomy types of deep learning architectures. Also, Binary Border Collie Optimization (BBCO) is introduced as an updated version of Border Collie Optimization (BCO) for feature reduction based on maximizing classification accuracy. The proposed computer aid diagnosis tool merges transfer deep learning ResNet101, BBCO feature reduction, and Support Vector Machine (SVM) classifier to form a hybrid model ResNet101-BBCO-SVM an accurate and fast model for classifying WBCs. As a result, the ResNet101-BBCO-SVM scores the best accuracy at 99.21%, compared to recent studies in the benchmark. The model showed that the addition of the BBCO algorithm increased the detection accuracy, and at the same time, decreased the classification time consumption. The effectiveness of the ResNet101-BBCO-SVM model has been demonstrated and beaten in reasonable ratios in recent literary studies and end-to-end transfer learning of pre-trained models.

Section: Related Workmentioning

confidence: 99%

A Novel Meta-Heuristic Optimization Algorithm in White Blood Cells燙lassification

Fathy¹,

Yaseen²,

Abou-Kreisha³

et al. 2023

“…To verify the novel hybrid technique (PSO-ACO), TSP benchmarks and empirical data are used to determine the best completion time. There is also a method based on ACO [22] that may be used to allocate channels to MANETs in order to achieve high spectral efficiency. To maximize spectrum usage and minimize interference, multi-objective functions are used.…”

Section: Related Workmentioning

confidence: 99%

Efficient Optimal Routing Algorithm Based on Reward and Penalty for Mobile Adhoc Networks

Anubha¹,

Bedi²,

Khan³

et al. 2023

Mobile adhoc networks have grown in prominence in recent years, and they are now utilized in a broader range of applications. The main challenges are related to routing techniques that are generally employed in them. Mobile Adhoc system management, on the other hand, requires further testing and improvements in terms of security. Traditional routing protocols, such as Adhoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), employ the hop count to calculate the distance between two nodes. The main aim of this research work is to determine the optimum method for sending packets while also extending life time of the network. It is achieved by changing the residual energy of each network node. Also, in this paper, various algorithms for optimal routing based on parameters like energy, distance, mobility, and the pheromone value are proposed. Moreover, an approach based on a reward and penalty system is given in this paper to evaluate the efficiency of the proposed algorithms under the impact of parameters. The simulation results unveil that the reward penalty-based approach is quite effective for the selection of an optimal path for routing when the algorithms are implemented under the parameters of interest, which helps in achieving less packet drop and energy consumption of the nodes along with enhancing the network efficiency.

“…However, the system classifies the dataset with high accuracy. Moreover, in [45][46][47][48][49][50][51][52][53][54][55], the authors use the theory of deep learning for various applications such as data mining and deep learning.…”

Section: Ye Et Al (2003)mentioning

confidence: 99%

Intelligent Medical Diagnostic System for Hepatitis B

Singh¹,

Prashar²,

Singla³

et al. 2022

Self Cite

The hepatitis B virus is the most deadly virus, which significantly affects the human liver. The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage; otherwise, it will become a severe problem and make a human liver suffer from the most dangerous diseases, such as liver cancer. In this paper, two medical diagnostic systems are developed for the diagnosis of this life-threatening virus. The methodologies used to develop these models are fuzzy logic and the neuro-fuzzy technique. The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developed models. The classification accuracy of a multilayered fuzzy inference system is 94%. The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System (ANFIS) classifies the result corresponding to the given input is 95.55%. The comparison of both developed models on the basis of their performance parameters has been made. It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system. Furthermore, the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease. In other words, the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.