Abstract
Primary recognition of pulmonary cancer nodules eloquently increases the odds of survival, also leads it solider problem to resolve, as it often relies on a tomography scan filmic examination. By increasing the possibility of effective treatment, earlier tumor diagnosis decreases lung cancer mortality. Radiologists usually diagnose lung cancer on medical images by a systematic analysis that consumes more time and is unreliable often, because of the substantial improvement in the transmission of data in the healthcare sector, the protection and integrity of medical data has been a huge problem for healthcare applications. This study utilizes computational intelligence techniques. For detection and data transmission, a novel Hybrid model is therefore proposed in this paper. Two steps are involved in the proposed method where diverse image processing procedures are used to detect cancer in the first step using MATLAB and data transfer to authorized persons via the IoT cloud in the second stage. The simulated steps include pre-processing, segmentation by Otsu thresholding along with swarm intelligence algorithm, extraction of features by local binary pattern and classification using the support vector machine (SVM). This work demonstrates the dominance of swarm-intelligent framework over the conventional algorithms in terms of performance metrics like sensitivity, accuracy and specificity as well as training time. The tests carried out show that the model built can achieve up to 92.96 percent sensitivity, 93.53 percent accuracy and 98.52 percent specificity.
Highlights
A malicious tumor characterized by uninhibited cell evolution in lung tissues is lung cancer
Several procedures have been established based on cross-sectional images, such as magnetic resonance imaging (MRI) or computed tomography (CT) or other topographic modes [3,4,5]
CT scan images are being used; they are analyzed by radiologists to recognize and identify nodules into malignant and benign nodules [10]
Summary
A malicious tumor characterized by uninhibited cell evolution in lung tissues is lung cancer. CT scan images are being used; they are analyzed by radiologists to recognize and identify nodules into malignant and benign nodules [10]. These techniques, require highly trained radiologists who are not in particular, accessible to people in remote regions. The existence of nodules that define a patient's destiny is very complex, as their shape and size differ from slice to slice They are often connected, such as arteries or bronchioles, to other pulmonary structures [12]. The structure of this paper contains Section II: related work, discusses about the www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 11, No 11, 2020 previous works, Section III proposed methodology represents the methods, block diagram and corresponding algorithms, Section IV shows segmentation with optimization concepts, Section V is the Extraction by LBP method, Section VI is Classification by SVM and Section VII presents the Simulation results, provides output images, statistical values and corresponding thingspeak plots
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