Cancer Detection and Prediction Using Genetic Algorithms

Bhandari, Aradhita; Tripathy, B. K.; Jawad, Khurram; Bhatia, Surbhi; Rahmani, Mohammad Khalid Imam; Mashat, Arwa

doi:10.1155/2022/1871841

Cited by 18 publications

(9 citation statements)

References 79 publications

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“…One of the main contributions of this work is applying a standard Genetic Algorithm (GA) to optimize each CNN layer in the models. The GA layer is used to optimize the weights in the CNN layer, which in turn improves the accuracy of the classification models [25,26,27]. Each model is trained and tested using three different encoding methods, label encoding, one-hot vector encoding, and finally using k-mer encoding.…”

Section: Classification Methodsmentioning

confidence: 99%

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

El-Tohamy¹,

Maghwary²,

Badr³

2022

IJACSA

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DNA sequence classification is one of the major challenges in biological data processing. The identification and classification of novel viral genome sequences drastically help in reducing the dangers of a viral outbreak like COVID-19. The more accurate the classification of these viruses, the faster a vaccine can be produced to counter them. Thus, more accurate methods should be utilized to classify the viral DNA. This research proposes a hybrid deep learning model for efficient viral DNA sequence classification. A genetic algorithm (GA) was utilized for weight optimization with Convolutional Neural Networks (CNN) architecture. Furthermore, Long Short-Term Memory (LSTM) as well as Bidirectional CNN-LSTM model architectures are employed. Encoding methods are needed to transform the DNA into numeric format for the proposed model. Three different encoding methods to represent DNA sequences as input to the proposed model were experimented: k-mer, label encoding, and one hot vector encoding. Furthermore, an efficient oversampling method was applied to overcome the imbalanced dataset issues. The performance of the proposed GA optimized CNN hybrid model using label encoding achieved the highest classification accuracy of 94.88% compared with other encoding methods.

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Section: Classification Methodsmentioning

confidence: 99%

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

El-Tohamy¹,

Maghwary²,

Badr³

2022

IJACSA

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“…e authors in [47][48][49][50][51][52][53] have developed a novel temporal attention encoder-decoder model, called MTSMFF, for multivariate time series. e authors in [58] have used genetic algorithms for detection and prediction of cancer and critically analysed the state-of-the-art techniques used for cancer research. e authors in [59] have implemented a model with machine learning techniques such as support vector machine, logistic regression, and K-nearest neighbor (KNN) in breast cancer classification.…”

Section: Related Workmentioning

confidence: 99%

A Novel Hybrid Convolutional Neural Network Approach for the Stomach Intestinal Early Detection Cancer Subtype Classification

Ahmed

2022

Computational Intelligence and Neuroscience

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There may be different types of cancer that cause fatal effects in the human body. In general, cancer is nothing but the unnatural growth of blood cells in different parts of the body and is named accordingly. It may be skin cancer, breast cancer, uterus cancer, intestinal cancer, stomach cancer, etc. However, every type of cancer consists of unwanted blood cells which cause issues in the body starting from the minor to death. Cancer cells have the common features in them, and these common features we have used in our work for the processing. Cancer has a significant death rate; however, it is frequently curable with simple surgery if detected in its early stages. A quick and correct diagnosis may be extremely beneficial to both doctors and patients. In several medical domains, the latest deep-learning-based model’s performance is comparable to or even exceeds that of human specialists. We have proposed a novel methodology based on a convolutional neural network that may be used for almost all types of cancer detection. We have collected different datasets of different types of common cancer from different sources and used 90% of the sample data for the training purpose, then we reduced it by 10%, and an 80% image set was used for the validation of the model. After that for testing purposes, we fed a sample dataset and obtain the results. The final output clearly shows that the proposed model outperforms the previous model when we compared our methodology with the existing work.

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“…Bhandari et al in [ 21 ] performed a comprehensive analysis of the state-of-the-art methods to detect cancer utilizing genetic algorithms. The authors made a deep analysis to identify the future challenges in the development of such techniques.…”

Section: Introductionmentioning

confidence: 99%

“…The authors focused on the type of cancer, functions being used, the main purpose of the methods, and the type of data being tested and verified. Additional information can be in [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

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Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier

Shawly

Alsheikhy

2022

Computational Intelligence and Neuroscience

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Leukemia cancer is the most common type of cancer that occurs in childhood. The most common types are acute lymphocytic leukemia (ALL) and acute myelogenous leukemia (AML) which affect children and adults, respectively. Several health issues occur due to these cancers. Leukemia affects the bone marrow or the lymph nodes. Leukemia produces abnormal white blood cells via the bone marrow system. The affected white blood cells are unable to perform their tasks properly. Detecting leukemia usually requires taking a blood smear from a patient and working with expert hematologists who analyze the smear with a microscope. In this paper, a method to detect ALL and AML using a deep learner classifier is developed and proposed. The method detects both types, determines their severity, and creates a message that recommends next steps to patients. This approach works based on image segmentation and a convolutional neural network (CNN) tool called AlexNet. The obtained results from the proposed approach and using MATLAB reached more than 98% accuracy. The margin exists because several operations are needed to fully detect the blood cancer. A dataset of leukemia from the Kaggle site is used to test the developed method and illustrate its effectiveness. This dataset is C-NMC_Leukemia, and it consists of nearly 10 GB worth of 15,000 images. A confusion matrix of testing images is provided to prove the correctness of the presented approach. Furthermore, a comparative analysis between the proposed algorithm and some works from the literature is presented. This analysis compares the method used to extract features, the classifier that is utilized, the accuracy, the precision, and the recall. The obtained results indicate that the proposed method outperforms other works and produces better results.

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Cancer Detection and Prediction Using Genetic Algorithms

Cited by 18 publications

References 79 publications

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

A Novel Hybrid Convolutional Neural Network Approach for the Stomach Intestinal Early Detection Cancer Subtype Classification

Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier

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