Prediction of Lung Cancer at Early Stage Using Correlation Analysis and Regression Modelling

Surendiran, J.; Kumar, K.Dinesh; Sathiya, T.; Selvam, S. Periyar; Vidhya, R.G.; Balaji, N.

doi:10.1109/ccip57447.2022.10058630

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…TP is the number of malignant tumours properly anticipated. The program properly finds malignant brain tumours [19]. The number of accurately predicted negative instances (non-cancerous tumours) is TN.…”

Section: Feature Extractionmentioning

confidence: 99%

Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method

Balasubramani Ramesh,

Sudhagar Dhandapani,

Sanda Sri Harsha

et al. 2023

ARASET

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Brain tumour identification and categorization are critical for diagnosis and treatment. This work uses preprocessing and classification algorithms to discover and categories brain tumours. Gaussian smoothing reduces noise and improves image quality, Genetic Algorithms select and optimize features, Deep Learning-Based Segmentation accurately segments tumours, Local Binary Patterns (LBP) extract texture features, and K-Nearest Neighbors classify tumours. Gaussian smoothing reduces noise and improves brain imaging data. Genetic Algorithms extract the most relevant and discriminative features from preprocessed photos. A Deep Learning-Based Segmentation model accurately segments brain tumour regions using these features. After segmentation, Local Binary Patterns (LBP) extract tumour texture features. The K-Nearest Neighbors (KNN) method classifies tumours using these texture features to capture tumour spatial patterns. Our suggested brain tumour detection and classification method combines several techniques to increase accuracy and reliability. Gaussian smoothing and LBP feature extraction improve feature discrimination. Deep Learning-Based Segmentation and the KNN classifier ensure precise tumour location and robust classification. The proposed method will be tested on brain scans including tumour areas. Classification performance measures include accuracy, sensitivity, specificity, and AUC. This work will improve brain tumour detection and classification methods for more accurate diagnoses and treatment planning. The primary goal of the work is to enhance brain tumour identification and categorization using pre processing, classification algorithms, and advanced techniques, ultimately improving diagnosis and treatment outcomes.

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Section: Feature Extractionmentioning

confidence: 99%

Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method

Balasubramani Ramesh,

Sudhagar Dhandapani,

Sanda Sri Harsha

et al. 2023

ARASET

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show abstract

“…When using Adaptive Thresholding, the threshold is determined by calculating the weighted mean of the pixel values in an adjacent region. [18] The weights themselves are selected using a Gaussian distribution. Adaptive Thresholding is an image processing technique.…”

Section: B Preprocessingmentioning

confidence: 99%

Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

Reddy,

M L,

Praveena

et al. 2023

IJRITCC

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Cancer of the breast is a malignant tumour that originates in the cells of the breast tissue. It is by far the most common kind of cancer found in females around the world, with a projected 2.3 million new cases will be discovered in the year 2020 alone. It is projected that one in eight women will be diagnosed with breast cancer at some point in their life, despite the fact that breast cancer can also occur in men. Breast cancer is a complex condition that can arise from a diverse set of factors, express itself in a variety of ways, and can be treated in a variety of ways. Ductal carcinoma in situ, invasive ductal carcinoma, and invasive lobular carcinoma are all different subtypes. Both the available treatment options and the expected outcome of breast cancer are very variable depending on the particular subtype of the illness. Breast cancer risk factors include drinking alcohol and not getting enough exercise, as well as getting older, having a family history of the disease, having genetic mutations, being exposed to estrogens, and having a family history of the disease. There is not always a connection between having risk factors and developing breast cancer, despite the fact that there can be a link between the two. The prognosis and treatment options for breast cancer are highly dependent on the stage of the disease at the time of diagnosis. During staging, the extent to which the cancer has spread throughout the body and how far it has progressed are both measured. The TNM system, the IAFCM system, the ACM system, and the MPIG system are just few of the staging systems that are used to classify breast cancer. These staging systems consider not only the size of the tumor but also whether or not lymph nodes are involved and whether or not distant metastases are present. The severity of breast cancer symptoms can vary widely, depending not only on the subtype of the disease but also on how far along it has progressed. Alterations in the size or shape of the breast, discharge from the nipple, and alterations in the skin of the breast (such as redness or dimpling) are all common indications. On the other hand, not all cases of breast cancer present themselves in a visible manner, and mammography and other forms of routine screening may be able to detect some of these cases. Options for treating breast cancer vary depending on the patient's condition and the stage of the disease, as well as the patient's overall health and their preferences towards therapy. Common examples of medical interventions include surgery, radiotherapy, chemotherapy, hormone therapy, and targeted therapy. Other examples include. In certain cases, it may be appropriate to participate in more than one form of treatment.

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An Analysis and Study of Brain Cancer with RNN Algorithm based AI Technique

Vallathan,

Rao Yanamadni,

Vidhya

et al. 2023

2023 7th International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Prediction of Lung Cancer at Early Stage Using Correlation Analysis and Regression Modelling

Cited by 9 publications

References 12 publications

Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method

Recent Advancement in Prediction and Analyzation of Brain Tumour using the Artificial Intelligence Method

Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

An Analysis and Study of Brain Cancer with RNN Algorithm based AI Technique

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