Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Enhanced Detection of Breast Cancer Microcalcifications in MRI via Hybrid LOOP and Haralick Features with Stacked Autoencoder Classification

Journal of Robotics and Control (JRC) · Vol. 6 No. 6 (2025) · hlm. 2729–2745

DOI
10.18196/jrc.v6i6.26502
ISSN
2715-5072, 2715-5056
Terbit
2025-11-20
Penerbit
Universitas Muhammadiyah Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Breast cancer is a prevalent and life-threatening disease among women worldwide, where early detection is crucial for effective treatment. Magnetic Resonance Imaging (MRI) is widely utilized for identifying breast tumors due to its superior sensitivity and spatial resolution. However, accurately detecting microcalcifications—small calcium deposits linked to malignancy—remains a significant challenge with conventional imaging analysis techniques like Haralick texture features alone. To overcome these limitations, this study introduces a novel hybrid feature extraction method that combines Local Optimal Oriented Patterns (LOOP) with Haralick texture descriptors, enhancing the representation of microcalcifications in breast MRI scans. The proposed method includes preprocessing the MRI images using Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise, followed by segmentation performed through morphological operations, multilevel Otsu thresholding, and region-growing approaches to isolate relevant breast tissues. Extracted hybrid features are then input into a Stacked Autoencoder-based Deep Neural Network (DNN) classifier to differentiate between malignant and benign cases. An experimental evaluation was conducted on a BI-RADS MRI dataset comprising over 900 clinical breast images. The hybrid feature extraction approach demonstrated improved diagnostic performance compared to traditional methods, achieving an accuracy of 95.33%, a sensitivity of 95.81%, a specificity of 94.67%, a positive predictive value (PPV) of 94.82%, and a negative predictive value (NPV) of 94.55%. These results represent a 3.83% accuracy increase over baseline classifiers such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN), and Random Forests (RF). Our findings confirm that integrating LOOP with Haralick features significantly enhances breast cancer detection from MRI images, thereby providing a robust tool to assist clinicians in early diagnosis and improving patient outcomes.

Kata kunci

Jaringan kolaborasi para penulis

28 penulis · 74 tautan kepenulisan bersama · 19 lintas organisasi

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Jain University
  • AISSMS Institute of Information Technology
  • KLS Gogte Institute of Technology
  • SRM Institute of Science and Technology
  • M. Kumarasamy College of Engineering
  • Sahyadri College of Engineering & Management
  • Nitte University
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Abdul KareemAfiliasi tidak diketahui4
Ezhilarasan GanesanJain University2
Sanjana PoojaryAfiliasi tidak diketahui1
T ThilagrajAfiliasi tidak diketahui1
Utkrishna Udesh NaikAfiliasi tidak diketahui1
R BharathAfiliasi tidak diketahui1
Melwin D SouzaSahyadri College of Engineering & Management1
Ananth Prabhu GurpurAfiliasi tidak diketahui1
Ranjith BhatJain University1
Rajashree NambiarNitte University1
Akshatha NaikAfiliasi tidak diketahui1
Ashwini AshwiniAfiliasi tidak diketahui1
Navilgone Krishna VaishnaviAfiliasi tidak diketahui1
Ruchitha Kamath SubhashchandraAfiliasi tidak diketahui1
Trapthi TrapthiAfiliasi tidak diketahui1
Pradeep B. ManeAISSMS Institute of Information Technology1
Melwin D SouzaYenepoya University1
N. KumareshAfiliasi tidak diketahui1
Sookshma AdigaAfiliasi tidak diketahui1
Sudhakar KAfiliasi tidak diketahui1
Suren Kumar SahuGandhi Institute for Technological Advancement1
A. AnandhKamaraj College1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.umy.ac.id/index.php/jrc/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Enhanced Detection of Breast Cancer Microcalcifications in MRI via Hybrid LOOP and Haralick Features with Stacked Autoencoder Classification</dc:title>
	<dc:creator>D Souza, Melwin</dc:creator>
	<dc:creator>Siddamsetti, Swapna</dc:creator>
	<dc:creator>Mulani, Altaf Osman</dc:creator>
	<dc:creator>Subbaiah, S</dc:creator>
	<dc:creator>Kumara, Varuna</dc:creator>
	<dc:creator>Ramya, S.</dc:creator>
	<dc:subject xml:lang="en-US">Breast Cancer Detection</dc:subject>
	<dc:subject xml:lang="en-US">Breast MRI</dc:subject>
	<dc:subject xml:lang="en-US">Microcalcification Detection</dc:subject>
	<dc:subject xml:lang="en-US">Hybrid Texture Features</dc:subject>
	<dc:subject xml:lang="en-US">LOOP Descriptor</dc:subject>
	<dc:subject xml:lang="en-US">Stacked Autoencoder</dc:subject>
	<dc:description xml:lang="en-US">Breast cancer is a prevalent and life-threatening disease among women worldwide, where early detection is crucial for effective treatment. Magnetic Resonance Imaging (MRI) is widely utilized for identifying breast tumors due to its superior sensitivity and spatial resolution. However, accurately detecting microcalcifications—small calcium deposits linked to malignancy—remains a significant challenge with conventional imaging analysis techniques like Haralick texture features alone. To overcome these limitations, this study introduces a novel hybrid feature extraction method that combines Local Optimal Oriented Patterns (LOOP) with Haralick texture descriptors, enhancing the representation of microcalcifications in breast MRI scans. The proposed method includes preprocessing the MRI images using Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise, followed by segmentation performed through morphological operations, multilevel Otsu thresholding, and region-growing approaches to isolate relevant breast tissues. Extracted hybrid features are then input into a Stacked Autoencoder-based Deep Neural Network (DNN) classifier to differentiate between malignant and benign cases. An experimental evaluation was conducted on a BI-RADS MRI dataset comprising over 900 clinical breast images. The hybrid feature extraction approach demonstrated improved diagnostic performance compared to traditional methods, achieving an accuracy of 95.33%, a sensitivity of 95.81%, a specificity of 94.67%, a positive predictive value (PPV) of 94.82%, and a negative predictive value (NPV) of 94.55%. These results represent a 3.83% accuracy increase over baseline classifiers such as Support Vector Machines (SVM), K-Nearest Neighbours (KNN), and Random Forests (RF). Our findings confirm that integrating LOOP with Haralick features significantly enhances breast cancer detection from MRI images, thereby providing a robust tool to assist clinicians in early diagnosis and improving patient outcomes.</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Muhammadiyah Yogyakarta</dc:publisher>
	<dc:date>2025-11-20</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.umy.ac.id/index.php/jrc/article/view/26502</dc:identifier>
	<dc:identifier>10.18196/jrc.v6i6.26502</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Robotics and Control (JRC); Vol. 6 No. 6 (2025); 2729-2745</dc:source>
	<dc:source>2715-5072</dc:source>
	<dc:source>2715-5056</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.umy.ac.id/index.php/jrc/article/view/26502/12025</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Melwin D Souza, Swapna Siddamsetti, Altaf Osman Mulani, S Subbaiah, Varuna Kumara, S. Ramya</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>