Evidence map›Paper›PMID 36091996›Full record

ArticlePeerJ. Computer science2022

A high-performance, hardware-based deep learning system for disease diagnosis.

Ali Siddique, Muhammad Azhar Iqbal, Muhammad Aleem, Jerry Chun-Wei Lin

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.5field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 4 countries.

Ali SiddiqueNational University of Computer and Emerging Sciences, Lahore Campus, Pakistan.
Muhammad Azhar IqbalLancaster University, Lancaster, United Kingdom.
Muhammad AleemNational University of Computer and Emerging Sciences, Islamabad, Pakistan.
Jerry Chun-Wei LinWestern Norway University of Applied Sciences, Bergen, Norway.
National University of Computer and Emerging Sciences · PKLancaster University · GBWestern Norway University of Applied Sciences · NO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Modern deep learning schemes have shown human-level performance in the area of medical science. However, the implementation of deep learning algorithms on dedicated hardware remains a challenging task because modern algorithms and neuronal activation functions are generally not hardware-friendly and require a lot of resources. Recently, researchers have come up with some hardware-friendly activation functions that can yield high throughput and high accuracy at the same time. In this context, we propose a hardware-based neural network that can predict the presence of cancer in humans with 98.23% accuracy. This is done by making use of cost-efficient, highly accurate activation functions, Sqish and LogSQNL. Due to its inherently parallel components, the system can classify a given sample in just one clock cycle,

Indexed as

Activation functionCancer diagnosisDeep learningField programmable gate arrayHardware friendlyNeural networksSwish

Identifiers

PMID36091996
PMCPMC9454880
OpenAlexW4285793798

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.