Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

Prasad, Jason; Zamani, Abu Sarwar; Arumugam, K.; Naved, Mohd; Pallathadka, Harikumar; Sammy, F.; Raghuvanshi, Abhishek; Kaliyaperumal, Karthikeyan

doi:10.1155/2022/1918379

Cited by 119 publications

(40 citation statements)

References 25 publications

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“…The median amount of time someone survives after receiving a diagnosis of advanced multiple myeloma is one year. Asbestos, a substance that was used extensively around the world in the 1970s and 1980s, has a significant connection to mesothelioma (MM) [ 1 , 2 ].…”

Section: Introductionmentioning

confidence: 99%

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

Shobana

Balasraswathi

Radhika

et al. 2022

BioMed Research International

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Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always results in death. Chemotherapy, surgery, radiation therapy, and immunotherapy are all potential treatments for multiple myeloma; however, the majority of patients are identified with the disease at an advanced stage, at which time it is resistant to these therapies. After obtaining a diagnosis of advanced multiple myeloma, the average length of time that a person lives is one year after hearing this news. There is a substantial link between asbestos exposure and mesothelioma (MM). Using an approach that enables feature selection and machine learning, this article proposes a classification and detection method for mesothelioma cancer. The CFS correlation-based feature selection approach is first used in the feature selection process. It acts as a filter, selecting just the traits that are relevant to the categorization. The accuracy of the categorization model is improved as a direct consequence of this. After that, classification is carried out with the help of naive Bayes, fuzzy SVM, and the ID3 algorithm. Various metrics have been utilized during the process of measuring the effectiveness of machine learning strategies. It has been discovered that the choice of features has a substantial influence on the accuracy of the categorization.

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Section: Introductionmentioning

confidence: 99%

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

Shobana

Balasraswathi

Radhika

et al. 2022

BioMed Research International

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“…If the backdrop is cluttered with distracting objects, then it might be difficult to isolate the area of focus where the symptoms are most prominent. A lack of control over the capture settings may result in pictures that are harder to anticipate and hence more difficult to identify diseases [6,7].…”

Section: Introductionmentioning

confidence: 99%

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Ansari

Jawarneh²,

Ritonga

et al. 2022

Journal of Food Quality

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In recent years, agricultural image processing research has been a key emphasis. Image processing techniques are used by computers to analyze images. New advancements in image capture and data processing have simplified the resolution of a wide range of agricultural concerns. Crop disease classification and identification are crucial for the agricultural industry’s technical and commercial well-being. In agriculture, image processing begins with a digital color picture of a diseased leaf. Plant health and disease detection must be monitored on a regular basis in property agriculture. Plant diseases have had a tremendous impact on civilization and the Earth as a whole. Extensions of detection strategies and classification methods try to identify and categorize each ailment that affects the plant rather than focusing on a single disease among several illnesses and symptoms. This article describes a new support vector machine and image processing-enabled approach for detecting and classifying grape leaf disease. The given architecture includes steps for image capture, denoising, enhancement, segmentation, feature extraction, classification, and detection. Image denoising is conducted using the mean function, image enhancement is performed using the CLAHE method, pictures are segmented using the fuzzy C Means algorithm, features are retrieved using PCA, and images are eventually classed using the PSO SVM, BPNN, and random forest algorithms. The accuracy of PSO SVM is higher in performing classification and detection of grape leaf diseases.

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“…Biopsies are an example of a minimally invasive operation that may be guided by CT scans. CT scans can also be used to plan and carry out radiation treatments for cancer, as well as plan and carry out other procedures [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor

Prasad

Prasad²,

Sawale³

et al. 2022

BioMed Research International

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The liver is in charge of a plethora of tasks that are critical to healthy health. One of these roles is the conversion of food into protein and bile, which are both needed for digestion. Inhaled and possibly harmful chemicals are flushed from the body. It destroys numerous nutrients acquired through the gastrointestinal system and limits the release of cholesterol by utilizing vitamins, carbohydrates, and minerals stored in the liver. The body’s tissues are made up of tiny structures known as cells. Cells proliferate and divide in order to create new ones in the normal sequence of events. When an old or damaged cell has to be replaced, a new cell must be synthesized. In other circumstances, the procedure is a total and utter failure. If the tissues of dead or damaged cells that have been cleared from the body are not removed, they may give birth to nodules and tumors. The liver can produce two types of tumors: benign and malignant. Malignant tumors are more dangerous to one’s health than benign tumors. This article presents a technique for the classification and identification of liver cancers that is based on image processing and machine learning. The approach may be found here. During the preprocessing stage of picture creation, the fuzzy histogram equalization method is applied in order to bring about a reduction in image noise. After that, the photographs are divided into many parts in order to zero down on the area of interest. For this particular classification task, the RBF-SVM approach, the ANN method, and the random forest method are all applied.

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Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis

Cited by 119 publications

References 25 publications

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique

Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease

Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor

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