Detection-of-Subtypes-of-Lung-and-Colon-Cancer-Using-CNN

INTRODUCTION

The growth and spread of the body's abnormal cells out of control is a characteristic shared by a group of illnesses collectively referred to as cancer. The ability of these cells to spread to other areas of the body through the lymphatic or circulatory systems is known as metastasis. Lung and colon cancer come in a variety of forms, each with special traits and methods of therapy. Under a microscope, lung cancer may be generally classified into two separate types based on how the cancer cells appear: Non-small cell lung cancer (NSCLC) accounts for the greater percentage of lung cancer cases (85%). Adenocarcinoma, squamous cell carcinoma, and giant cell carcinoma are among the subtypes of NSCLC that can be further subdivided. Additionally, colon cancer can be categorized according to the kind of cell that gives rise to it: 1. Adenocarcinoma: This is the most common kind of colon cancer, accounting for around 95% of cases. Adenocarcinomas arise from the glandular cells lining the inner surface of the colon. 2. Carcinoid tumors: These are an uncommon kind of colon cancer that start in the colon's hormoneproducing cells. A patient may get chemotherapy, radiation therapy, immunotherapy, targeted therapy, or other treatments for cancer, depending on the kind and stage of the disease. As early identification and treatment can improve the chance of recovery and survival, routine screenings and check-ups are essential to the prevention and management of cancer.

Characterizing histopathological imaging data of many cancer kinds, such as skin, breast, lung, colon, and colorectal cancer, has garnered significant attention. There are several applications for ML, DL, and TL in the detection of lung and colon cancer.

1. Using the ML technique to detect lung and colon cancer: Masood et al. (2021) proposed an ML method based on DL that analyzed pathological pictures of lung and colon cancers to identify five distinct tissue types. Following feature extraction using the 2D Fourier and 2D Wavelet (2D FW) methods, they merged the features and used them to train a CNN model. The findings showed that the recommended design was the most accurate in identifying cancerous tissues, with a 96.33 percent rate.
2. Using CAD to detect lung and colon cancer: (Nishiio et al., 2021) described an automated CAD system for classifying pictures of lung tissue histopathology. They evaluated eight machine learning methods on two datasets, including both traditional texture analysis (TA) and homologybased image processing (HI) to extract visual characteristics. The CAD system with HI outperformed the TA system in both datasets. They came to the conclusion that HI was far more effective for CAD systems than TA and that this may lead to the development of an accurate CAD system for lung tissues. Moreover, (Shandilya and Nayak) created a CAD technique in 2022 for categorizing lung tissue histology pictures. They used a collection of histological pictures of lung tissue that was made accessible to the public for the development and validation of CAD. To extract features from the picture, multiscale processing was applied.

METHODOLOGY

1. Data Collection: This stage involves gathering from several sources the pertinent medical pictures of the colon and lung. These pictures may be from a colonoscopy, an MRI, or a CT scan.
2. Data Pre-processing: To eliminate any noise or artifacts, the gathered photos are pre-processed. After that, the pictures are standardized and scaled to a standard size in order to feed them into the CNN algorithm.
3. CNN Model Training: The pre-processed pictures are used to train a CNN model. Multiple convolutional layers in the model learn attributes from the photos, and then fully connected layers categorize the images as either malignant or non-cancerous.
4. Model Testing: The Trained CNN model is then examined using another set of pictures that weren't utilized to teach the example. The model's performance is assessed using many measures, such as ROC, specificity, accuracy, and sensitivity bend.
5. Interpretation: After obtaining the model tested and taught, the outcomes are analyzed to determine the effectiveness of the suggested approach. Moreover, Modifications or advancements can be modified the model in light of the outcomes. It's critical to remember that the particulars of each stage may vary. Based on the specific dataset and research objectives. As an example, the CNN model construction and data enhancement techniques, and choosing the hyperparameter can all have a substantial impact on the model's execution. To generate precise and reliable outcomes, it is essential to appropriately schedule and execute each stage of the method.

SYSTEM IMPLEMENTATION

There are many processes involved in implementing a Convolutional Neural Network (CNN) algorithmbased system for the diagnosis of lung and colon cancer. This is a high-level summary of the procedure:

1. Gather a sizable collection of medical photos showing the colon and lungs, including samples that are malignant and those that are not. The pictures are accessible to the general public through databases or medical facilities.
2. Pre-process the photos to make sure they are of the same size and quality and to get rid of any noise or artifacts that might skew the model's results.
3. Create more training pictures by transforming the original photos in different ways, such rotating, resizing, and flipping. This enhances the training data's variety and strengthens the model's capacity for generalization.
4. Make use of a CNN algorithm that is appropriate for identifying lung and colon cancer. To determine the ideal setup, this may entail testing with various layers and hyperparameters.
5. Use the pre-processed and supplemented dataset to train the CNN. To reduce the prediction error, this entails feeding the CNN the pictures and modifying the model's parameters.
6. Test the trained CNN's sensitivity, specificity, and accuracy in identifying malignant areas using an independent set of test pictures.
7. Include the CNN model that has been trained into a software program that can receive fresh medical pictures and produce a forecast of whether the picture shows a colon or lung in good condition or has cancerous regions.
8. Install the system in a clinical environment and track its effectiveness over time to make sure it is accurate and dependable in supporting the identification and management of colon and lung cancer.

RESULTS

CONCLUSION

To sum up, CNN-based algorithms for the identification of lung and colon cancer have shown promising results in accurately identifying cancerous regions in medical images. These technologies have the potential to significantly improve cancer diagnosis and treatment results because they can detect cancer early, reduce the need for unnecessary biopsies, and raise the accuracy of diagnoses. However, there are a number of limitations that limit the effectiveness of these systems, including the likelihood of false positives and false negatives, interpretability concerns, and dependence on high-quality data. CNN-based lung and colon cancer detection systems should be used in conjunction with other diagnostic tools and by trained medical professionals to provide a precise and reliable cancer diagnosis. Future advancements and integration of CNN-based lung and colon cancer detection systems into clinical practice are highly promising. As medical image collections grow, we should expect these systems to become more accurate. Additionally, advancements in CNN designs and training techniques should make it possible for these systems to be integrated with other diagnostic tools for personalized treatment.