A Fast Accurate Deep Learning Framework for Prediction of All Cancer Types

Fadel, Magdy M.; Elseddeq, Nadia G.; Arnous, Reham; Ali, Zainab H.; El-Desouky, Ali I.

doi:10.1109/access.2022.3222365

Cited by 5 publications

(6 citation statements)

References 45 publications

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“…Table 5 shows the output performance of various classification method. 0.971 Decision Tree [31] 0.945 K-NN [31] 0.952 Random Forests [31] 0.96…”

Section: J_n = σ(W_j * [G_(n-1) X_n] + B_j) (4)mentioning

confidence: 99%

“…Precise cancer prognosis prediction is performed through Whale Optimization Algorithm. The method outperforms with an execution time of about 4113 seconds [31]. ReliefF + CNN hybrid method improved accuracy in prediction ovarian, leukemia, and Central Nervous System (CNS) cancer datasets [32].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Table 4 shows the breast cancer accuracy percentage. Radom Forest [31] 99.8 96.5 Decision Tree [31] 98.8 95.1…”

Section: J_n = σ(W_j * [G_(n-1) X_n] + B_j) (4)mentioning

confidence: 99%

“…Figure 16 shows the PSO and LSTM output and target for cancer dataset. [31] 0.94 0.92 0.93 1 K-NN [31] 0.92 0.91 0.91 1 Random Forests [31] 0.96 0.94 0.95 1…”

Section: J_n = σ(W_j * [G_(n-1) X_n] + B_j) (4)mentioning

confidence: 99%

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Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Gini,

Padmakala

2024

Preprint

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Gene data set collected from a diverse population gene expression profiles, genetic variations, and clinical attributes for earlier detection cancer. Time Series Forecasting (TSF) techniques are applied and exploits temporal dependencies within the gene data, enables the prediction of breast cancer and progression. The proposed methodology such as Particle Swarm Optimization-Long Short Term Memory (PSO & LSTM) and Cat Swarm Optimization -Long Short Term Memory (CSO & LSTM) combines with gene data augmentation and analyse the temporal patterns breast cancer genes. Receiver Operating Characteristic (ROC) curve is used for evaluation the proposed models predictive performance. The proposed methods are validated in traditional dataset and collected gene data sets, from National Center for Biotechnology Information (NCBI). The results are compared with existing classification model and evaluated the effectiveness of the TSF methods such as of CSO-LSTM and PSO-LSTM in prediction of breast cancer diseases. The proposed methods contribute to early detection by leveraging time series forecasting techniques. The proposed model improves the accuracy of and reliability of breast cancer prediction, which enables health professional with more information and potentially enhances the patient outcomes

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“…Table 5 shows the output performance of various classification method. 0.971 Decision Tree [31] 0.945 K-NN [31] 0.952 Random Forests [31] 0.96…”

Section: J_n = σ(W_j * [G_(n-1) X_n] + B_j) (4)mentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…Table 4 shows the breast cancer accuracy percentage. Radom Forest [31] 99.8 96.5 Decision Tree [31] 98.8 95.1…”

Section: J_n = σ(W_j * [G_(n-1) X_n] + B_j) (4)mentioning

confidence: 99%

“…Figure 16 shows the PSO and LSTM output and target for cancer dataset. [31] 0.94 0.92 0.93 1 K-NN [31] 0.92 0.91 0.91 1 Random Forests [31] 0.96 0.94 0.95 1…”

Section: J_n = σ(W_j * [G_(n-1) X_n] + B_j) (4)mentioning

confidence: 99%

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Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Gini,

Padmakala

2024

Preprint

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“…There are five categories of AF based on [1], namely bounded, rectified, non-linear below, non-linear and unbounded above, and increasing and decreasing functions. The sigmoid function is a bounded function broadly used in many FNN applications [11,16,23,28]. It is a smooth and continuous function that maps real value [−∞, ∞] into [0, 1] [7].…”

Section: Introductionmentioning

confidence: 99%

The effect of amplitude modification in S-shaped activation functions on neural network regression

Makhrus

2023

NNW

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Time series forecasting using multilayer feed-forward neural networks (FNN) is potential to give high accuracy. Several factors influence the accuracy. One of them is the choice of activation functions (AFs). There are several AFs commonly used in FNN with their specific characteristics, such as bounded type AFs. They include sigmoid, softsign, arctan, and tanh. This paper investigates the effect of the amplitude in the bounded AFs on the FNNs accuracy. The theoretical investigations use simplified FNN models: linear equation and linear combination. The results show that the higher amplitudes give higher accuracy than typical amplitudes in softsign, arctan, and tanh AFs. However, in sigmoid AF, the amplitude changes do not influence the accuracy. These theoretical results are supported by experiments using the FNN model for time series prediction of 10 foreign exchanges from different continents compared to the US dollar. Based on the experiments, the optimum amplitude of the AFs should be high, that is greater or equal to 100 times of the maximum input values to the FNN, and the accuracy gains up to 310 times.

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RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection

et al. 2023

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Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained; in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced.

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A Fast Accurate Deep Learning Framework for Prediction of All Cancer Types

Cited by 5 publications

References 45 publications

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

Prediction of Breast Cancer Diseases From Genes Using Pso and Cso Tuned Long Short-term Memory

The effect of amplitude modification in S-shaped activation functions on neural network regression

RETRACTED ARTICLE: Multi-stage biomedical feature selection extraction algorithm for cancer detection

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