Automated Cancer Detection and Classification

Detection of cancer is very difficult and need more concern in the field of medical. This paper studies the various causes, types, symptoms of cancer. The main objective of this paper is to design an efficient and effective approach to detect the cancer using image processing. The research proposed an improved hybrid approach for detection and segmentation. The research work includes improvement of microscopic image, then segmentation of cells, extract the features of cancer and then at the final stage it described the classification step. After using different approaches and review several previous approaches this technique used an efficient and appropriate method to design a proposed framework.

Authors

Institutions

Publication Details

Journal
Dinasti Health and Pharmacy Science
Published
2025-04-21
DOI
https://doi.org/10.38035/dhps.v2i2.1811
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automated Cancer Detection and Classification

Aa Hubur, Andino Maseleno, Arsya Sinatria Artha
Dinasti Health and Pharmacy Science
Machine Learning in Bioinformatics
article

Automated Cancer Detection and Classification

Aa Hubur, Andino Maseleno, Arsya Sinatria Artha
article en

Abstract

Detection of cancer is very difficult and need more concern in the field of medical. This paper studies the various causes, types, symptoms of cancer. The main objective of this paper is to design an efficient and effective approach to detect the cancer using image processing. The research proposed an improved hybrid approach for detection and segmentation. The research work includes improvement of microscopic image, then segmentation of cells, extract the features of cancer and then at the final stage it described the classification step. After using different approaches and review several previous approaches this technique used an efficient and appropriate method to design a proposed framework.

Dinasti Health and Pharmacy ScienceVol. 2(2)
Universitas Tunas Pembangunan (ID), International Open University
Openalex Percentile: Top 92%
Machine Learning in Bioinformatics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.