Lung cancer medical image recognition using Deep Neural Networks

Jakimovski, Goran; Davčev, Danco

doi:10.1109/icdim.2018.8847136

Cited by 19 publications

(5 citation statements)

References 3 publications

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“…In the era of DL with the concept of transfer learning, we observed some works that utilized the same in their work. For example, Jakimovski and Davcev [104] gathered CT images of 70 subjects from a local medical hospital and labelled them from oncology specialists. Two piles were created, where 58 subjects had cancer, and 12 were diagnosed cancer-free.…”

Section: Anifah Et Almentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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Section: Anifah Et Almentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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“…This work is helpful in early detection of cancer and early treatment. Main purpose of this research is early detection and early treatment [28]. Researcher believes thatdevelopment of AI and computer software that could better employ radiomic data in order to translate them to practical usefulness.…”

Section: Applications Of Ai In Medical Imagingmentioning

confidence: 99%

Artificial Intelligence Techniques for Cancer Detection in Medical Image Processing: A Review

kaur¹

2021

TURCOMAT

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Cancer is the uncontrolled growth of abnormal cells in any part of a body. Cancer is a broad term for a group of diseases caused when abnormal cells grows in different body parts. There are more than hundred types of Cancer such as Lung cancer, Breast cancer, Skin cancer, Oral cancer, Colon cancer and Prostate cancer. Delay in treatment can cause serious health issues, even cause loss of life. This paper gives the review on methods of detection of lung cancer and brain cancer and liver using image processing. The methods used for detection are Automated and computer-aided detection system (CAD) with artificial intelligence and these methods are good to process a large datasets to provide accurate and efficient results in the detection of cancer. However, these processing system have to face many challenges to implement on large scale including imageacquisition, pre-processing, segmentation, and data management and classification strategies to be compatible with AI. This paper reviews the various image acquisition and segmentation techniques. These techniques become the need of an hour to cater the growing patient population and for the improvement in the Healthcare system.

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“…2. DCNN is used to detect lung cancer based on chest CT images [21]. Lung regions are extracted from CT images in the first stage.…”

Section: A Lung Cancer Detectionmentioning

confidence: 99%

Stage Identification and Classification of Lung Cancer using Deep Convolutional Neural Network

Prakash¹,

Vas.P²

2020

IJACSA

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The performance of lung segmentation is highly dependent on disease prediction task. Challenges for prediction and segmentation raise the need of using multiple learning techniques. Current models initially perform image segmentation in all CT scan images and then classify it as malicious or benign. This consumes more time since it segments both normal and abnormal CT's. So, due to improper segmentation of images the region of interest will be inaccurate and results in false classification of images. Therefore, by initially checking the CT which has malignancy and then segmenting those lesions will provide more accuracy in segmentation of cancerous nodules thereby helps to identify the stage of cancer the patient is suffering from. The aim is to improve the current cancer detection techniques using DCNN by filtering out malignant CT scan from the medical dataset and segmenting those images for stage identification. Segmentation is done using UNET++ architecture and stage identification is done by considering the "size" (T) parameter from the globally recognized standard named "TNM staging" for classifying the spread of each malignant nodule as T1-T4. 99.83 % accuracy is achieved in lung cancer classification using VGG-16 which yields better results for both segmentation and stage identification too.

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Lung cancer medical image recognition using Deep Neural Networks

Cited by 19 publications

References 3 publications

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Artificial Intelligence Techniques for Cancer Detection in Medical Image Processing: A Review

Stage Identification and Classification of Lung Cancer using Deep Convolutional Neural Network

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