FSPBO-DQN: SeGAN based segmentation and Fractional Student Psychology Optimization enabled Deep Q Network for skin cancer detection in IoT applications

Kumar, K. Suresh; Suganthi, N.; Muppidi, Satish; Kumar, B. Santhosh

doi:10.1016/j.artmed.2022.102299

Cited by 24 publications

(10 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides U-net and its variant implementations, there are many other deep convolutional neural networks proposed for the same task. For example, Generative Adversarial Network (GAN) [26,27,28] was also popular for medical image segmentation. For the ultrasound images, Torrents-Barrena et al [29] segmented the placenta using a conditional Generative Adversarial Network (cGAN), and then segmented the placental vessels using a modified spatial kernelized fuzzy C-means algorithm and Markov random field.…”

Section: Deep Convolutional Neural Network For Medical Image Segmenta...mentioning

confidence: 99%

A dual path encoder-decoder network for placental vessel segmentation in fetoscopic surgery

2024

KSII TIIS

View full text Add to dashboard Cite

A fetoscope is an optical endoscope, which is often applied in fetoscopic laser photocoagulation to treat twin-to-twin transfusion syndrome. In an operation, the clinician needs to observe the abnormal placental vessels through the endoscope, so as to guide the operation. However, low-quality imaging and narrow field of view of the fetoscope increase the difficulty of the operation. Introducing an accurate placental vessel segmentation of fetoscopic images can assist the fetoscopic laser photocoagulation and help identify the abnormal vessels. This study proposes a method to solve the above problems. A novel encoderdecoder network with a dual-path structure is proposed to segment the placental vessels in fetoscopic images. In particular, we introduce a channel attention mechanism and a continuous convolution structure to obtain multi-scale features with their weights. Moreover, a switching connection is inserted between the corresponding blocks of the two paths to strengthen their relationship. According to the results of a set of blood vessel segmentation experiments conducted on a public fetoscopic image dataset, our method has achieved higher scores than the current mainstream segmentation methods, raising the dice similarity coefficient, intersection over union, and pixel accuracy by 5.80%, 8.39% and 0.62%, respectively.

show abstract

Section: Deep Convolutional Neural Network For Medical Image Segmenta...mentioning

confidence: 99%

A dual path encoder-decoder network for placental vessel segmentation in fetoscopic surgery

2024

KSII TIIS

View full text Add to dashboard Cite

show abstract

“…This method used traditional ML approaches, including XGBoost supervised ML, CNNs, and EfficientNet. In [15], a potential skin cancer detection method was proposed, named Fractional Student Psychology Based Optimization-based Deep Q Network (FSPBO-based DQN), in a wireless network scenario. At first, an image was given to the preprocessing stage, where a Type II fuzzy system and the cuckoo search optimized (T2FCS) method were used to eliminate the noise of images.…”

Section: Related Workmentioning

confidence: 99%

“…method produces smooth variation with lower memory requirements and effectual computational efficiency. Based on Adam, the bias is formulated by: `a = `a − bc d e √f g a h (15) where is the step size, i a g indicates corrected bias, = g a refers to the bias-corrected second-moment evaluation, ε signifies the constant, and θ l-1 represents the parameter at the prior time instant (l-1). The corrected bias of the first-order moment was formulated by:…”

Section: Wwwetasrcom Rajeshkumar Et Al: Blockchain-assisted Homomorph...mentioning

confidence: 99%

Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model

Ananth

Mohananthini³

2023

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Blockchain (BC) and Machine learning (ML) technologies have been investigated for potential applications in medicine with reasonable success to date. On the other hand, as accurate and early diagnosis of skin lesion classification is essential to gradually increase the survival rate of the patient, Deep-Learning (DL) and ML technologies were introduced for supporting dermatologists to overcome these challenges. This study designed a Blockchain Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using an Optimal Deep Learning (BHESKD-ODL) model. The presented BHESKD-ODL model achieves security and proper classification of skin lesion images using BC to store the medical images of the patients to restrict access to third-party users or intruders. In addition, the BHESKD-ODL method secures the medical images using the mayfly optimization (MFO) algorithm with the Homomorphic Encryption (HE) technique. For skin lesion diagnosis, the proposed BHESKD-ODL method uses pre-processing and the Adam optimizer with a Fully Convolutional Network (FCN) based segmentation process. Furthermore, a radiomics feature extraction with a Bidirectional Recurrent Neural Network (BiRNN) model was employed for skin lesion classification. Finally, the Red Deer Optimization (RDO) algorithm was used for the optimal hyperparameter selection of the BiRNN approach. The experimental results of the BHESKD-ODL system on a benchmark skin dataset proved its promising performance in terms of different measures.

show abstract

“…Routing is an important phenomenon in IoT and it has the facility to handle the dynamic nature of network topology. It is more significant to select the best routing path to transfer the data packets such that the procedure of routing is created by the proposed SPWO approach [41]. The features associated with SPBO are inherited by WOA to derive a hybrid solution to accomplish the task of data routing in an IoT network.…”

Section: Routing Using Spwo Algorithmmentioning

confidence: 99%

An IoT-Based Framework and Ensemble Optimized Deep Maxout Network Model for Breast Cancer Classification

Peta

Koppu

2022

Electronics

View full text Add to dashboard Cite

Internet of Things (IoT) plays an essential role in the area of the healthcare system. IoT devices provide information about patients in the healthcare monitoring framework. Moreover, patients can examine their health with smart devices and hence IoT is a major factor in all aspects of the health care management system. Breast cancer is a deadly cancer in women and the detection of this disease at the primary stage increases the survival rate. Due to the computational complexity associated with acquiring features, classification results generated from the existing methods are unsatisfactory and hence it is important to design a method using deep learning concepts for classifying cancer disease. An efficient and robust classification model named Student Psychology Whale Optimization-based Deep maxout network with optimization (SPWO-based Deep maxout network) classifies breast cancer disease. The advantage of using a Deep maxout network is that it effectively learns intrinsic features from the data. The weight factor of the deep learning model is updated with respect to iteration based on the fitness measure that in turn results in higher results by acquiring a minimal error value. However, the proposed model obtains outstanding accuracy, sensitivity, and specificity in terms of testing with the values of 0.931, 0.953, and 0.915 with 100 nodes.

show abstract

FSPBO-DQN: SeGAN based segmentation and Fractional Student Psychology Optimization enabled Deep Q Network for skin cancer detection in IoT applications

Cited by 24 publications

References 28 publications

A dual path encoder-decoder network for placental vessel segmentation in fetoscopic surgery

A dual path encoder-decoder network for placental vessel segmentation in fetoscopic surgery

Blockchain-Assisted Homomorphic Encryption Approach for Skin Lesion Diagnosis using Optimal Deep Learning Model

An IoT-Based Framework and Ensemble Optimized Deep Maxout Network Model for Breast Cancer Classification

Contact Info

Product

Resources

About