Authors :
Raj Kumar Khatri; Dr. Neelesh Jain; Prateek Singhal
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
https://tinyurl.com/4xm7xhxj
Scribd :
https://tinyurl.com/4mnduwkc
DOI :
https://doi.org/10.5281/zenodo.8416845
Abstract :
The illness that claims the most lives is lung
cancer. It begins with the tissues that are responsible for
breathing. The majority of cancer patients have lung
cancer, and the survival rate is the same for men and
women. Lung cancer is most commonly brought on by
smoking, however there are several industrial asbestos
products that can harm our lungs and result in lung
cancer. Lung cancer can fall into one of two types, the
first of which is benign, which is thought to be caused by
malignant cells but is less hazardous since it can be
treated. It is the earliest stage of lung cancer and only
affects a small number of tissues. The second type is
malignant, which is serious and deadly and can kill a
person. It is strongly advised to begin receiving therapies
as soon as possible becauseit is the second and final stage
of lung cancer, which is scarcely treatable. Numerous
researchers have studied it and attempted to develop a
way to identify it in its early stages, while cells are still in
the benign state. Lung cancer may be accurately
identified through image processing, which is a field of
study. There are several methods for implementing it,
but accuracy and false alarm rate are important. System
should take false alarm rates very seriously and should
have high, reliable accuracy. The shape of malignant
cells and blood vessels are extracted using Prewitt edge
detection in the suggested approach. The proposed
approach employs Support Vector Machine (SVM) as
well to identify normal and pathological cells so that it
can decide right away which grade each one belongs to.
The IQ-OTHNCCD dataset, which has a total of 1097
pictures for benign, malignant, and normal categories,
was used to evaluate the proposed approach. Compared
to previously implemented systems, the system
maintained a high degree of accuracy.
Keywords :
Lung Cancer, Prewitt Edge Detection, SupportVector Machine, Benign, Malignant, CT-Scan.
The illness that claims the most lives is lung
cancer. It begins with the tissues that are responsible for
breathing. The majority of cancer patients have lung
cancer, and the survival rate is the same for men and
women. Lung cancer is most commonly brought on by
smoking, however there are several industrial asbestos
products that can harm our lungs and result in lung
cancer. Lung cancer can fall into one of two types, the
first of which is benign, which is thought to be caused by
malignant cells but is less hazardous since it can be
treated. It is the earliest stage of lung cancer and only
affects a small number of tissues. The second type is
malignant, which is serious and deadly and can kill a
person. It is strongly advised to begin receiving therapies
as soon as possible becauseit is the second and final stage
of lung cancer, which is scarcely treatable. Numerous
researchers have studied it and attempted to develop a
way to identify it in its early stages, while cells are still in
the benign state. Lung cancer may be accurately
identified through image processing, which is a field of
study. There are several methods for implementing it,
but accuracy and false alarm rate are important. System
should take false alarm rates very seriously and should
have high, reliable accuracy. The shape of malignant
cells and blood vessels are extracted using Prewitt edge
detection in the suggested approach. The proposed
approach employs Support Vector Machine (SVM) as
well to identify normal and pathological cells so that it
can decide right away which grade each one belongs to.
The IQ-OTHNCCD dataset, which has a total of 1097
pictures for benign, malignant, and normal categories,
was used to evaluate the proposed approach. Compared
to previously implemented systems, the system
maintained a high degree of accuracy.
Keywords :
Lung Cancer, Prewitt Edge Detection, SupportVector Machine, Benign, Malignant, CT-Scan.
