Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier

Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning

Lung cancer (LC) is a life-threatening and dangerous disease all over the world. However, earlier diagnoses and treatment can save lives. Earlier diagnoses of malevolent cells in the lungs responsible for oxygenating the human body and expelling carbon dioxide due to significant procedures are critical. Even though a computed tomography (CT) scan is the best imaging approach in the healthcare sector, it is challenging for physicians to identify and interpret the tumour from CT scans. LC diagnosis in CT scan using artificial intelligence (AI) can help radiologists in earlier diagnoses, enhance performance, and decrease false negatives. Deep learning (DL) for detecting lymph node contribution on histopathological slides has become popular due to its great significance in patient diagnoses and treatment. This study introduces a computer-aided diagnosis for LC by utilizing the Waterwheel Plant Algorithm with DL (CADLC-WWPADL) approach. The primary aim of the CADLC-WWPADL approach is to classify and identify the existence of LC on CT scans. The CADLC-WWPADL method uses a lightweight MobileNet model for feature extraction. Besides, the CADLC-WWPADL method employs WWPA for the hyperparameter tuning process. Furthermore, the symmetrical autoencoder (SAE) model is utilized for classification. An investigational evaluation is performed to demonstrate the significant detection outputs of the CADLC-WWPADL technique. An extensive comparative study reported that the CADLC-WWPADL technique effectively performs with other models with a maximum accuracy of 99.05% under the benchmark CT image dataset.

A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques

Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure for segmentation and classification with a large set of data modalities can be a challenging task. Therefore, a crucial requirement is to significantly develop the computer-assisted diagnostic system intended for the initial cancer identification. This article offers a systematic review of Deep Learning approaches using various image modalities to detect multi-organ cancers from 2012 to 2023. It emphasizes the detection of five supreme predominant tumors, i.e., breast, brain, lung, skin, and liver. Extensive review has been carried out by collecting research and conference articles and book chapters from reputed international databases, i.e., Springer Link, IEEE Xplore, Science Direct, PubMed, and Wiley that fulfill the criteria for quality evaluation. This systematic review summarizes the overview of convolutional neural network model architectures and datasets used for identifying and classifying the diverse categories of cancer. This study accomplishes an inclusive idea of ensemble deep learning models that have achieved better evaluation results for classifying the different images into cancer or healthy cases. This paper will provide a broad understanding to the research scientists within the domain of medical imaging procedures of which deep learning technique perform best over which type of dataset, extraction of features, different confrontations, and their anticipated solutions for the complex problems. Lastly, some challenges and issues which control the health emergency have been discussed.

An Improved Archimedes Optimization-aided Multi-scale Deep Learning Segmentation with dilated ensemble CNN classification for detecting lung cancer using CT images

Early detection of lung cancer is necessary to prevent deaths caused by lung cancer. But, the identification of cancer in lungs using Computed Tomography (CT) scan based on some deep learning algorithms does not provide accurate results. A novel adaptive deep learning is developed with heuristic improvement. The proposed framework constitutes three sections as (a) Image acquisition, (b) Segmentation of Lung nodule, and (c) Classifying lung cancer. The raw CT images are congregated through standard data sources. It is then followed by nodule segmentation process, which is conducted by Adaptive Multi-Scale Dilated Trans-Unet3+. For increasing the segmentation accuracy, the parameters in this model is optimized by proposing Modified Transfer Operator-based Archimedes Optimization (MTO-AO). At the end, the segmented images are subjected to classification procedure, namely, Advanced Dilated Ensemble Convolutional Neural Networks (ADECNN), in which it is constructed with Inception, ResNet and MobileNet, where the hyper parameters is tuned by MTO-AO. From the three networks, the final result is estimated by high ranking-based classification. Hence, the performance is investigated using multiple measures and compared among different approaches. Thus, the findings of model demonstrate to prove the system's efficiency of detecting cancer and help the patient to get the appropriate treatment.

An Advanced Lung Carcinoma Prediction and Risk Screening Model Using Transfer Learning

Lung cancer, also known as lung carcinoma, has a high death rate, but an early diagnosis can substantially reduce this risk. In the current era, prediction models face challenges such as low accuracy, excessive noise, and low contrast. To resolve these problems, an advanced lung carcinoma prediction and risk screening model using transfer learning is proposed. Our proposed model initially preprocesses lung computed tomography images for noise removal, contrast stretching, convex hull lung region extraction, and edge enhancement. The next phase segments the preprocessed images using the modified Bates distribution coati optimization (B-RGS) algorithm to extract key features. The PResNet classifier then categorizes the cancer as normal or abnormal. For abnormal cases, further risk screening determines whether the risk is low or high. Experimental results depict that our proposed model performs at levels similar to other state-of-the-art models, achieving enhanced accuracy, precision, and recall rates of 98.21%, 98.71%, and 97.46%, respectively. These results validate the efficiency and effectiveness of our suggested methodology in early lung carcinoma prediction and risk assessment.

Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques

Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison.

Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis

Histopathology is the most accurate way to diagnose cancer and identify prognostic and therapeutic targets. The likelihood of survival is significantly increased by early cancer detection. With deep networks' enormous success, significant attempts have been made to analyze cancer disorders, particularly colon and lung cancers. In order to do this, this paper examines how well deep networks can diagnose various cancers using histopathology image processing. This work intends to increase the performance of deep learning architecture in processing histopathology images by constructing a novel fine-tuning deep network for colon and lung cancers. Such adjustments are performed using regularization, batch normalization, and hyperparameters optimization. The suggested fine-tuned model was evaluated using the LC2500 dataset. Our proposed model's average precision, recall, F1-score, specificity, and accuracy were 99.84%, 99.85%, 99.84%, 99.96%, and 99.94%, respectively. The experimental findings reveal that the suggested fine-tuned learning model based on the pre-trained ResNet101 network achieves higher results against recent state-of-the-art approaches and other current powerful CNN models.

Lung cancer histopathological image classification using wavelets and AlexNet

Among malignant tumors, lung cancer has the highest morbidity and fatality rates worldwide. Screening for lung cancer has been investigated for decades in order to reduce mortality rates of lung cancer patients, and treatment options have improved dramatically in recent years. Pathologists utilize various techniques to determine the stage, type, and subtype of lung cancers, but one of the most common is a visual assessment of histopathology slides. The most common subtypes of lung cancer are adenocarcinoma and squamous cell carcinoma, lung benign, and distinguishing between them requires visual inspection by a skilled pathologist. The purpose of this article was to develop a hybrid network for the categorization of lung histopathology images, and it did so by combining AlexNet, wavelet, and support vector machines. In this study, we feed the integrated discrete wavelet transform (DWT) coefficients and AlexNet deep features into linear support vector machines (SVMs) for lung nodule sample classification. The LC25000 Lung and colon histopathology image dataset, which contains 5,000 digital histopathology images in three categories of benign (normal cells), adenocarcinoma, and squamous carcinoma cells (both are cancerous cells) is used in this study to train and test SVM classifiers. The study results of using a 10-fold cross-validation method achieve an accuracy of 99.3% and an area under the curve (AUC) of 0.99 in classifying these digital histopathology images of lung nodule samples.

Machine learning and deep learning approach for medical image analysis: diagnosis to detection

Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows tremendous growth in the medical field. Medical images are considered as the actual origin of appropriate information required for diagnosis of disease. Detection of disease at the initial stage, using various modalities, is one of the most important factors to decrease mortality rate occurring due to cancer and tumors. Modalities help radiologists and doctors to study the internal structure of the detected disease for retrieving the required features. ML has limitations with the present modalities due to large amounts of data, whereas DL works efficiently with any amount of data. Hence, DL is considered as the enhanced technique of ML where ML uses the learning techniques and DL acquires details on how machines should react around people. DL uses a multilayered neural network to get more information about the used datasets. This study aims to present a systematic literature review related to applications of ML and DL for the detection along with classification of multiple diseases. A detailed analysis of 40 primary studies acquired from the well-known journals and conferences between Jan 2014-2022 was done. It provides an overview of different approaches based on ML and DL for the detection along with the classification of multiple diseases, modalities for medical imaging, tools and techniques used for the evaluation, description of datasets. Further, experiments are performed using MRI dataset to provide a comparative analysis of ML classifiers and DL models. This study will assist the healthcare community by enabling medical practitioners and researchers to choose an appropriate diagnosis technique for a given disease with reduced time and high accuracy.

A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification

Lung cancer is the primary reason of cancer deaths worldwide, and the percentage of death rate is increasing step by step. There are chances of recovering from lung cancer by detecting it early. In any case, because the number of radiologists is limited and they have been working overtime, the increase in image data makes it hard for them to evaluate the images accurately. As a result, many researchers have come up with automated ways to predict the growth of cancer cells using medical imaging methods in a quick and accurate way. Previously, a lot of work was done on computer-aided detection (CADe) and computer-aided diagnosis (CADx) in computed tomography (CT) scan, magnetic resonance imaging (MRI), and X-ray with the goal of effective detection and segmentation of pulmonary nodule, as well as classifying nodules as malignant or benign. But still, no complete comprehensive review that includes all aspects of lung cancer has been done. In this paper, every aspect of lung cancer is discussed in detail, including datasets, image preprocessing, segmentation methods, optimal feature extraction and selection methods, evaluation measurement matrices, and classifiers. Finally, the study looks into several lung cancer-related issues with possible solutions.

Artificial Intelligence Techniques to Predict the Airway Disorders Illness: A Systematic Review

Airway disease is a major healthcare issue that causes at least 3 million fatalities every year. It is also considered one of the foremost causes of death all around the globe by 2030. Numerous studies have been undertaken to demonstrate the latest advances in artificial intelligence algorithms to assist in identifying and classifying these diseases. This comprehensive review aims to summarise the state-of-the-art machine and deep learning-based systems for detecting airway disorders, envisage the trends of the recent work in this domain, and analyze the difficulties and potential future paths. This systematic literature review includes the study of one hundred fifty-five articles on airway diseases such as cystic fibrosis, emphysema, lung cancer, Mesothelioma, covid-19, pneumoconiosis, asthma, pulmonary edema, tuberculosis, pulmonary embolism as well as highlights the automated learning techniques to predict them. The study concludes with a discussion and challenges about expanding the efficiency and machine and deep learning-assisted airway disease detection applications.

Cell Recognition Using BP Neural Network Edge Computing

This exploration is to solve the efficiency and accuracy of cell recognition in biological experiments. Neural network technology is applied to the research of cell image recognition. The cell image recognition problem is solved by constructing an image recognition algorithm. First, with an in-depth understanding of computer functions, as a basic intelligent algorithm, the artificial neural network (ANN) is widely used to solve the problem of image recognition. Recently, the backpropagation neural network (BPNN) algorithm has developed into a powerful pattern recognition tool and has been widely used in image edge detection. Then, the structural model of BPNN is introduced in detail. Given the complexity of cell image recognition, an algorithm based on the ANN and BPNN is used to solve this problem. The BPNN algorithm has multiple advantages, such as simple structure, easy hardware implementation, and good learning effect. Next, an image recognition algorithm based on the BPNN is designed and the image recognition process is optimized in combination with edge computing technology to improve the efficiency of algorithm recognition. The experimental results show that compared with the traditional image pattern recognition algorithm, the recognition accuracy of the designed algorithm for cell images is higher than 93.12%, so it has more advantages for processing the cell image algorithm. The results show that the BPNN edge computing can improve the scientific accuracy of cell recognition results, suggesting that edge computing based on the BPNN has a significant practical value for the research and application of cell recognition.

Early Prediction of Lung Cancers Using Deep Saliency Capsule and Pre-Trained Deep Learning Frameworks

Lung cancer is the cellular fission of abnormal cells inside the lungs that leads to 72% of total deaths worldwide. Lung cancer are also recognized to be one of the leading causes of mortality, with a chance of survival of only 19%. Tumors can be diagnosed using a variety of procedures, including X-rays, CT scans, biopsies, and PET-CT scans. From the above techniques, Computer Tomography (CT) scan technique is considered to be one of the most powerful tools for an early diagnosis of lung cancers. Recently, machine and deep learning algorithms have picked up peak energy, and this aids in building a strong diagnosis and prediction system using CT scan images. But achieving the best performances in diagnosis still remains on the darker side of the research. To solve this problem, this paper proposes novel saliency-based capsule networks for better segmentation and employs the optimized pre-trained transfer learning for the better prediction of lung cancers from the input CT images. The integration of capsule-based saliency segmentation leads to the reduction and eventually reduces the risk of computational complexity and overfitting problem. Additionally, hyperparameters of pretrained networks are tuned by the whale optimization algorithm to improve the prediction accuracy by sacrificing the complexity. The extensive experimentation carried out using the LUNA-16 and LIDC Lung Image datasets and various performance metrics such as accuracy, precision, recall, specificity, and F1-score are evaluated and analyzed. Experimental results demonstrate that the proposed framework has achieved the peak performance of 98.5% accuracy, 99.0% precision, 98.8% recall, and 99.1% F1-score and outperformed the DenseNet, AlexNet, Resnets-50, Resnets-100, VGG-16, and Inception models.

Ensemble Learning Framework with GLCM Texture Extraction for Early Detection of Lung Cancer on CT Images

Lung cancer has emerged as a major cause of death among all demographics worldwide, largely caused by a proliferation of smoking habits. However, early detection and diagnosis of lung cancer through technological improvements can save the lives of millions of individuals affected globally. Computerized tomography (CT) scan imaging is a proven and popular technique in the medical field, but diagnosing cancer with only CT scans is a difficult task even for doctors and experts. This is why computer-assisted diagnosis has revolutionized disease diagnosis, especially cancer detection. This study looks at 20 CT scan images of lungs. In a preprocessing step, we chose the best filter to be applied to medical CT images between median, Gaussian, 2D convolution, and mean. From there, it was established that the median filter is the most appropriate. Next, we improved image contrast by applying adaptive histogram equalization. Finally, the preprocessed image with better quality is subjected to two optimization algorithms, fuzzy c-means and k-means clustering. The performance of these algorithms was then compared. Fuzzy c-means showed the highest accuracy of 98%. The feature was extracted using Gray Level Cooccurrence Matrix (GLCM). In classification, a comparison between three algorithms—bagging, gradient boosting, and ensemble (SVM, MLPNN, DT, logistic regression, and KNN)—was performed. Gradient boosting performed the best among these three, having an accuracy of 90.9%.

Exhaled Breath Volatile Organic Compound Analysis for the Detection of Lung Cancer- A Systematic Review

A rapid and effective diagnostic method is essential for lung cancer since it shows symptoms only at its advanced stage. Research is being carried out in the area of exhaled breath analysis for the diagnosis of various pulmonary diseases including lung cancer. In this method exhaled breath volatile organic compounds (VOC) are analyzed with various techniques such as gas chromatography-mass spectrometry, ion mobility spectrometry, and electronic noses. The VOC analysis is suitable for lung cancer detection since it is non-invasive, fast, and also a low-cost method. In addition, this technique can detect primary stage nodules. This paper presents a systematic review of the various method employed by researchers in the breath analysis field. The articles were selected through various search engines like EMBASE, Google Scholar, Pubmed, and Google. In the initial screening process, 214 research papers were selected using various inclusion and exclusion criteria and finally, 55 articles were selected for the review. The results of the reviewed studies show that detection of lung cancer can be effectively done using the VOC analysis of exhaled breath. The results also show that this method can be used for detecting the different stages and histology of lung cancer. The exhaled breath VOC analysis technique will be popular in the future, bypassing the existing imaging techniques. This systematic review conveys the recent research opportunities, obstacles, difficulties, motivations, and suggestions associated with the breath analysis method for lung cancer detection.

An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy

Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for polyp identification. This strategy benefits from recent findings that different classification models can better learn and extract various information within the image. Therefore, our Ensemble classifier can derive a more consequential decision than each individual classifier. The extracted combined information inherits the ResNet's advantage of residual connection, while it also extracts objects when covered by occlusions through depth-wise separable convolution layer of the Xception model. Here, we applied our strategy to still frames extracted from a colonoscopy video. It outperformed other state-of-the-art techniques with a performance measure greater than 95% in each of the algorithm parameters. Our method will help researchers and gastroenterologists develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnoses that rely on image.

Recent Applications of Artificial Intelligence in Early Cancer Detection

Cancer is a deadly disease often caused by the accumulation of various genetic mutations and pathological alterations. The death rate can only be reduced when it is detected in the early stages because treatment of cancer when the tumor has not metastasized in many regions of the body is more effective. However, early cancer detection is fraught with difficulties. Advances in artificial intelligence (AI) have developed a new scope for efficient and early detection of such a fatal disease. AI algorithms have a remarkable ability to perform well on a variety of tasks that are presented or fed to the system. Numerous studies have produced machine learning and deep learning-assisted cancer prediction models to detect cancer from previously accessible data with better accuracy, sensitivity, and specificity. It has been observed that the accuracy of prediction models in classifying fed data as benign, malignant, or normal is improved by implementing efficient image processing techniques and data segmentation augmentation methodologies, along with advanced algorithms. In this review, recent AI-based models for the diagnosis of the most prevalent cancers in the breast, lung, brain, and skin have been analysed. Available AI techniques, data preparation, modeling processes, and performance assessments have been included in the review.

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.

Optimized lung tumor diagnosis system using enhanced version of crow search algorithm, Zernike moments, and support vector machine

Early detection of lung tumors is so important to heal this disease in the initial steps. Automatic computer-aided detection of this disease is a good method for reducing human mistakes and improving detection precision. The major concept here is to propose the best CAD system for lung tumor detection. In the presented technique, after pre-processing and segmentation of the lung area, its features including different orders of Zernike moments have been extracted. After features extraction, they have been injected into an optimized version of Support Vector Machine (SVM) for final diagnosis. The optimization of the SVM is based on an enhanced design of the Crow Search Algorithm (ECSA). For validating the proposed method, it was applied to three datasets including Lung CT-Diagnosis, TCIA, and RIDER Lung CT collection, and the results are validated by comparing with three state-of-the-art methods including Walwalker method, Mon method, and Naik method to indicate the system superiority toward the compared methods. The system is also analyzed based on different orders of Zernike moment to select the best order. The final results indicate that the suggested method has a suitable accuracy for diagnosing lung cancer.

State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016-2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

A Method for Optimal Detection of Lung Cancer Based on Deep Learning Optimized by Marine Predators Algorithm

Lung cancer is the uncontrolled growth of cells in the lung that are made up of two spongy organs located in the chest. These cells may penetrate outside the lungs in a process called metastasis and spread to tissues and organs in the body. In this paper, using image processing, deep learning, and metaheuristic, an optimal methodology is proposed for early detection of this cancer. Here, we design a new convolutional neural network for this purpose. Marine predators algorithm is also used for optimal arrangement and better network accuracy. The method finally applied to RIDER dataset, and the results are compared with some pretrained deep networks, including CNN ResNet-18, GoogLeNet, AlexNet, and VGG-19. Final results showed higher results of the proposed method toward the compared techniques. The results showed that the proposed MPA-based method with 93.4% accuracy, 98.4% sensitivity, and 97.1% specificity provides the highest efficiency with the least error (1.6) toward the other state of the art methods.