Pathological categorization of lung carcinoma from multimodality images using convolutional neural networks

Abstract

Accurate diagnosis and treatment of lung carcinoma depend on its pathological type and staging. Normally, pathological analysis is performed either by needle biopsy or surgery. Therefore, a noninvasive method to detect pathological types would be a good alternative. Hence, this work aims at categorizing different types of lung cancer from multimodality images. The proposed approach involves two stages. Initially, a Blind/Referenceless Image Spatial Quality Evaluator‐based approach is adopted to extract the slices having lung abnormalities from the dataset. The slices then are transferred to a novel shallow convolutional neural network model to detect adenocarcinoma, squamous cell carcinoma, and small cell carcinoma from multimodality images. The classifier efficacy is then investigated by comparing precision, recall, area under curve, and accuracy with pretrained models and existing methods. The results narrate that the suggested system outperformed with a testing accuracy of 95% in Positron emission tomography/computed tomography (PET/CT), 93% in CT images of the Lung‐PET‐CT‐DX dataset, and 98% in the Lung3 dataset. Furthermore, a kappa score of 0.92 in PET/CT of Lung‐PETCT‐DX and 0.98 in CT of Lung3 exhibited the effectiveness of the presented system in the field of lung cancer classification.

A transformer‐based deep neural network for detection and classification of lung cancer via PET/CT images

“…We have compared our approach to the study presented in Ref. 34 It is applied to our public Lung-PET-CT-Dx dataset and is based on a shallow CNN model.…”

“…We have compared our approach to the study presented in Ref. 34 It is applied to our public Lung‐PET‐CT‐Dx dataset and is based on a shallow CNN model. The results in Table 4 show that this model performs less than our proposed model.…”

“…Jacob et al 34 employed a CNN and a blind/ referenceless image spatial quality evaluator for histologic classification of lung cancer. Initially, a preprocessing step is used to extract the images with lung abnormalities from the dataset.…”

An Automated Diagnosis Method for Lung Cancer Target Detection and Subtype Classification-Based CT Scans

“…This paper conducts a detailed comparison of the proposed method with the latest methods in the field of lung cancer classification. The specific comparison results in references [31][32][33][34][35][36][37][38][39][40] are presented in Table 5. The first row of the table lists key information such as the data modality, dataset source, lung cancer subtypes, sample size, algorithm models, and final accuracy.…”

Attention augmented residual autoencoder for efficient polyp segmentation

“…Sabeena et al 20,21 investigated the performance ensemble-based stack classifier and unification of wavelet transform and CNN for efficient cervical cell classification and found that the CNN-based segmentation followed by ensemble-based stack classifier achieved the best performance for cervical cell classification. Chinnu et al 22 used Blind/Referenceless Image Spatial Quality Evaluator-based approach to extract the slices having lung abnormalities from the dataset followed by a novel shallow CNN model to detect lung carcinoma from multimodality images. Remya et al 23 proposed L-UNet and Eff-UNet architectures for segmentation of G banded metaphase chromosome images.…”