A Deep CNN Technique for Detection of Breast Cancer Using Histopathology Images

Gitanjali Wadhwa, Amandeep Kaur

Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) 2020 conference-paper cited by 24

Source More in Medicine & Health

importance 6/10 ai assisted AI in cancer detection breast cancer pathology stage: research

Problem

Detecting breast cancer early from histopathology images and reducing the diagnostic burden on doctors.

Why it matters

Early breast cancer detection improves survival outcomes and manual histopathology review is time-consuming for clinicians.

What the AI did

A deep CNN (DenseNet-201) extracted features from histopathology images and classified them as malignant or benign on the BreakHis dataset.

Results

Highest classification accuracy 95.58%, precision 0.90, recall 0.99, F1-score 0.89 on the BreakHis dataset

Impact

The evidence supports strong benchmark accuracy on one public dataset; no clinical validation or deployment is stated.

Record

Venue	Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA)
Published	2020
Work type	conference-paper
Authors	Gitanjali Wadhwa, Amandeep Kaur
Citations	24 (OpenAlex, at import time)
References	42
Model or method	DenseNet-201 (CNN)
Organization	not stated in source
Primary source	https://doi.org/10.1109/accthpa49271.2020.9213192
Open access	closed
Keywords	breast cancer, histopathology, DenseNet-201, CNN, classification, BreakHis

Importance rating

6 / 10

significant — thousands to millions affected

Early breast cancer detection and reducing clinician burden are significant; evidence limited to benchmark accuracy on one public dataset, no clinical validation stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

More in Medicine & Health

#	Paper	Year	Citations	Importance
1	Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction Yeong Chan Lee, Jiho Cha, Injeong Shim, Woong‐Yang Park, Se Woong Kang...	2023	107	10
2	Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization Tawsifur Rahman, Amith Abdullah Khandakar, Muhammad Abdul Kadir, Khand...	2020	651	10
3	A comparative analysis of machine learning classifiers for stroke prediction: A predictive analytics approach Nitish Biswas, Khandaker Mohammad Mohi Uddin, Sarreha Tasmin Rikta, Sa...	2022	116	10
4	Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning Hamada R. H. Al-Absi, Mohammad Tariqul Islam, Mahmoud Ahmed Refaee, Mu...	2022	89	10
5	Proteomics and Machine Learning Approaches Reveal a Set of Prognostic Markers for COVID-19 Severity With Drug Repurposing Potential Kruthi Suvarna, Deeptarup Biswas, Medha Gayathri J. Pai, Arup Acharjee...	2021	76	10