Fuzzy Inference System for Efficient Lung Cancer Detection

Tiwari, Laxmikant; Raja, Rohit; Sharma, Vaibhav; Miri, Rohit

doi:10.1007/978-981-13-8798-2_4

Cited by 12 publications

(7 citation statements)

References 6 publications

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“…This way, the users can take better decisions according to the risk and uncertainty of the system. This is why this method is referred (Das et al 2020) 2020 Medical disease analysis Feature Extraction Model using Neuro-Fuzzy for classification (Tiwari et al 2020) 2020 Lung Cancer Fuzzy Inference System for detection of lung cancer (Kour et al 2020) 2020 Medical disease analysis Neuro-fuzzy systems for prediction and classification of different types of diseases (Vidhya & Shanmugalakshmi, 2020) 2020 Medical disease analysis Modified-ANFIS using various disease analysis based on medical Big Data (Ranjit et al 2020) 2020 Knee Diseases Knee Diseases Prediction using adaptive and improved ANFIS (Hekmat et al 2020) 2020 Acute kidney Injury Risk factors, prevalence, and early outcome analysis of acute kidney injury (Sood et al 2020) 2020 dengue fever LDA-ANFIS based dengue fever risk assessment framework (Sujatha et al 2020) 2020 Breast cancer Micro calcifications in breast identification utilizing ANFIS (Liu et al 2019) 2019 Prostate Cancer Using a fuzzy inference system, prostate cancer was predicted (Turabieh et al 2019) 2019 Breast cancer A D-ANFIS is used to handle the missing values in the application used for the Internet of Medical Things (Mori et al 2019) 2019 Medical decision making Extracting the relationship between input and output of the learning data using fuzzy rules (de Medeiros et al 2017) 2017 Medical decision making Real-time medical diagnosis using a fuzzy inference system (Nguyen et al 2015) 2015 Breast cancer A new classifier based on the type-2 fuzzy logic system for breast cancer diagnosis (Azar & Hassanien, 2015) 2014 Breast cancer Medical big data dimensionality reduction using a neuro-fuzzy classifier (Papageorgio...…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.

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Section: Monte Carlo Simulationmentioning

confidence: 99%

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

et al. 2021

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“…Many intelligent systems with different applications were based on FIS [2][3][4]6,7]. Sagir et al [4] proposed two extended models of ANFIS to apply to the heart disease prediction problem.…”

Section: Fuzzy Inference System In Complex Fuzzy Setmentioning

confidence: 99%

M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

et al. 2020

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Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

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“…The authors concluded that the proposed novel method achieved competitive performances on the MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Dataset. Tiwari et al (2020) [ 34 ] displayed the application of the fuzzy inference system. The authors applied a pipeline consisting of preprocessing, image segmentation, feature extraction, and the application of fuzzy inference rules, which are capable of identifying lung cancer cells with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

Šegota

Lorencin

Smolić

et al. 2021

Biology

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Urinary bladder cancer is one of the most common cancers of the urinary tract. This cancer is characterized by its high metastatic potential and recurrence rate. Due to the high metastatic potential and recurrence rate, correct and timely diagnosis is crucial for successful treatment and care. With the aim of increasing diagnosis accuracy, artificial intelligence algorithms are introduced to clinical decision making and diagnostics. One of the standard procedures for bladder cancer diagnosis is computer tomography (CT) scanning. In this research, a transfer learning approach to the semantic segmentation of urinary bladder cancer masses from CT images is presented. The initial data set is divided into three sub-sets according to image planes: frontal (4413 images), axial (4993 images), and sagittal (996 images). First, AlexNet is utilized for the design of a plane recognition system, and it achieved high classification and generalization performances with an AUCmicro¯ of 0.9999 and σ(AUCmicro) of 0.0006. Furthermore, by applying the transfer learning approach, significant improvements in both semantic segmentation and generalization performances were achieved. For the case of the frontal plane, the highest performances were achieved if pre-trained ResNet101 architecture was used as a backbone for U-net with DSC¯ up to 0.9587 and σ(DSC) of 0.0059. When U-net was used for the semantic segmentation of urinary bladder cancer masses from images in the axial plane, the best results were achieved if pre-trained ResNet50 was used as a backbone, with a DSC¯ up to 0.9372 and σ(DSC) of 0.0147. Finally, in the case of images in the sagittal plane, the highest results were achieved with VGG-16 as a backbone. In this case, DSC¯ values up to 0.9660 with a σ(DSC) of 0.0486 were achieved. From the listed results, the proposed semantic segmentation system worked with high performance both from the semantic segmentation and generalization standpoints. The presented results indicate that there is the possibility for the utilization of the semantic segmentation system in clinical practice.

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Fuzzy Inference System for Efficient Lung Cancer Detection

Cited by 12 publications

References 6 publications

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991–2020)

M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Semantic Segmentation of Urinary Bladder Cancer Masses from CT Images: A Transfer Learning Approach

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